
@article{abdollahpouriMultistakeholderRecommendationSurvey2020,
  title = {Multistakeholder Recommendation: {{Survey}} and Research Directions},
  shorttitle = {Multistakeholder Recommendation},
  author = {Abdollahpouri, Himan and Adomavicius, Gediminas and Burke, Robin and Guy, Ido and Jannach, Dietmar and Kamishima, Toshihiro and Krasnodebski, Jan and Pizzato, Luiz},
  date = {2020-01-10},
  journaltitle = {User Modeling and User-Adapted Interaction},
  shortjournal = {User Model User-Adap Inter},
  issn = {0924-1868, 1573-1391},
  doi = {10.1007/s11257-019-09256-1},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\JBA8GK96\\Abdollahpouri et al. - 2020 - Multistakeholder recommendation Survey and resear.pdf},
  langid = {english}
}

@book{abdollahpouriPersonalizationResearchDirections2019,
  title = {Beyond {{Personalization}}: {{Research Directions}} in {{Multistakeholder Recommendation}}},
  shorttitle = {Beyond {{Personalization}}},
  author = {Abdollahpouri, Himan and Adomavicius, Gediminas and Burke, Robin and Guy, Ido and Jannach, Dietmar and Kamishima, Toshihiro and Krasnodebski, Jan and Pizzato, Luiz},
  date = {2019-05-01},
  abstract = {Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\YXJISA5R\\Abdollahpouri et al_2019_Beyond Personalization.pdf}
}

@article{al-houmailyRecoveryPerformanceAtomic,
  title = {Recovery and {{Performance}} of {{Atomic Commit Processs}}},
  author = {Al-houmaily, Yousef J and Samaras, George},
  pages = {49},
  abstract = {A transaction is traditionally de ned so as to provide the properties of atomicity, consistency, integrity, and durability (ACID) for any operation it performs. In order to ensure the atomicity of distributed transactions, an atomic commitprotocol needs to be followed by all sites participating in a transaction execution to agree on the nal outcome, that is, commit or abort. A variety of commit protocols have been proposed that either enhance the performance of the classical two-phase commit protocol during normal processing or reduce the cost of recovery processing after a failure. In this chapter, we survey a number of two-phase commit variants and optimizations, including some recent ones, providing an insight in the performance trade-o between normal and recovery processing. We also analyze the performance of a representative set of commit protocols both analytically as well as empirically using simulation.},
  langid = {english}
}

@article{aminiDiscoveringImpactKnowledge2011,
  title = {Discovering {{The Impact Of Knowledge In Recommender Systems}}: {{A Comparative Study}}},
  shorttitle = {Discovering {{The Impact Of Knowledge In Recommender Systems}}},
  author = {Amini, Bahram and Ibrahim, Roliana and Othman, Mohd},
  date = {2011-09-01},
  journaltitle = {International Journal of Computer Science \& Engineering Survey},
  shortjournal = {International Journal of Computer Science \& Engineering Survey},
  volume = {2},
  doi = {10.5121/ijcses.2011.2301},
  abstract = {Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3JILH3P2\\Amini et al_2011_Discovering The Impact Of Knowledge In Recommender Systems.pdf}
}

@inproceedings{andrzejakSoftwareConfigurationDiagnosis2018,
  title = {Software {{Configuration Diagnosis}} \textendash{} {{A Survey}} of {{Existing Methods}} and {{Open Challenges}}},
  booktitle = {Proceedings~of~ the~20th~{{International~Configuration~Workshop}}},
  author = {Andrzejak, Artur and Friedrich, Gerhard and Wotawa, Franz},
  date = {2018},
  pages = {85--92},
  url = {http://confws.ist.tugraz.at},
  abstract = {As software systems become more complex and featurerich, configuration mechanisms are needed to adapt them to different execution environments and usage profiles. As a consequence, failures due to erroneous configuration settings are becoming more common, calling for effective mechanisms for diagnosis, repair, and prevention of such issues. In this paper, we survey approaches for diagnosing software configuration errors, methods for debugging these errors, and techniques for testing against such issues. In addition, we outline current challenges of isolating and fixing faults in configuration settings, including improving fault localization, handling the case of multi-stack systems, and configuration verification at runtime.},
  eventtitle = {20th {{International Workshop}} on {{Configuration}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9Z76QC7P\\Andrzejak et al. - Software Conﬁguration Diagnosis – A Survey of Exis.pdf},
  langid = {english}
}

@article{article,
  title = {Collaborative Product Configuration: Formalization and Efficient Algorithms for Dependency Analysis.},
  author = {Mendon\c{c}a, Marc\'ilio and Cowan, Donald and Malyk, William and Oliveira, Toacy},
  date = {2008-01},
  journaltitle = {JSW},
  volume = {3},
  pages = {69--82}
}

@inproceedings{atasItemRecommendationUsing2017,
  title = {Beyond {{Item Recommendation}}: {{Using Recommendations}} to {{Stimulate Knowledge Sharing}} in {{Group Decisions}}},
  shorttitle = {Beyond {{Item Recommendation}}},
  booktitle = {Social {{Informatics}}},
  author = {Atas, M\"usl\"um and Felfernig, Alexander and Stettinger, Martin and Tran, Thi Ngoc Trang},
  editor = {Ciampaglia, Giovanni Luca and Mashhadi, Afra and Yasseri, Taha},
  date = {2017},
  pages = {368--377},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-67256-4_29},
  abstract = {The intensity of domain knowledge exchange among group members is an important factor that directly influences group decision quality. The more frequent information is exchanged among group members, the higher the quality of the corresponding decision. In this paper we present results of an empirical study conducted with groups of students \textendash{} the task of each group was to take a decision regarding the exam topics the group prefers. This group decision had to be taken on the basis of a group decision support environment with included recommendation functionality and a discussion forum that allows for information exchange among group members. Depending on the included variant of the group recommendation algorithm, groups received recommendations that varied in terms of recommendation diversity. The results of the study show that increased recommendation diversity leads to an increased degree of information exchange among group members.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\JEFM2ATK\\Atas et al_2017_Beyond Item Recommendation.pdf},
  isbn = {978-3-319-67256-4},
  keywords = {Decision quality,Group decision making,Group recommender systems,Information exchange},
  langid = {english},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@inproceedings{atasLiquidDemocracyGroupbased2018,
  title = {Liquid {{Democracy}} in {{Group}}-Based {{Configuration}}},
  author = {Atas, Muesluem and Tran, Thi Ngoc Trang and Samer, Ralph and Felfernig, Alexander and Stettinger, Martin and Fucci, Davide},
  date = {2018},
  pages = {93--98},
  abstract = {Group-based configuration systems support scenarios where a group of users configures a product/service. In those groupbased configuration scenarios where the knowledge of some group members regarding items is insufficient, an advice of experts is necessary in order to help members to evaluate products or services. This paper introduces a novel approach which takes advantage of the concept of liquid democracy that allows the delegation of group member votes to experts. Concerning the application of liquid democracy, we propose a new approach based on Multi-attribute Utility theory (MAUT)-based evaluation used to calculate the utility of configurable items. Compared to the traditional approach, the proposed MAUT-based evaluation focuses on the role of experts by assigning higher weights to them. Additionally, the respective expertise level of the experts is taken into account. Consequently, the main contribution of this paper consists in the improvement of group-based configuration by taking liquid democracy aspects into consideration.},
  eventtitle = {{{ConfWS}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\95YRVV9G\\Atas et al. - Liquid Democracy in Group-based Conﬁguration.pdf},
  langid = {english}
}

@inproceedings{atasSociallyAwareDiagnosisConstraintBased2019,
  title = {Socially-{{Aware Diagnosis}} for {{Constraint}}-{{Based Recommendation}}},
  booktitle = {Proceedings of the 27th {{ACM Conference}} on {{User Modeling}}, {{Adaptation}} and {{Personalization}}  - {{UMAP}} '19},
  author = {Atas, Muesluem and Samer, Ralph and Felfernig, Alexander and Tran, Thi Ngoc Trang and Erdeniz, Seda Polat and Stettinger, Martin},
  date = {2019},
  pages = {121--129},
  publisher = {{ACM Press}},
  location = {{Larnaca, Cyprus}},
  doi = {10.1145/3320435.3320436},
  abstract = {Constraint-based group recommender systems support the identification of items that best match the individual preferences of all group members. In cases where the requirements of the group members are inconsistent with the underlying constraint set, the group needs to be supported such that an appropriate solution can be found. In this paper, we present a guided approach that determines socially-aware diagnoses based on different aggregation functions. We analyzed the prediction quality of different aggregation functions by using data collected in a user study. The results indicate that those diagnoses guided by the Least Misery aggregation function achieve a higher prediction quality compared to the Average Voting, Most Pleasure, and Majority Voting. Moreover, another major outcome of our work reveals that diagnoses based on aggregation functions outperform basic approaches such as Breadth First Search and Direct Diagnosis.},
  eventtitle = {The 27th {{ACM Conference}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3CYBXHM5\\Atas et al. - 2019 - Socially-Aware Diagnosis for Constraint-Based Reco.pdf},
  langid = {english}
}

@thesis{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017,
  title = {M\"oglichkeiten intelligenter Empfehlungssysteme in der Produktkonfikuration},
  author = {Benz, Daniel},
  date = {2017-09-14},
  institution = {{Hochschule Karlsruhe \textendash{} Technik und Wirtschaft}},
  location = {{Kalrsruhe}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\8HENYXLR\\Benz - Karlsruhe, 13. September 2017.pdf},
  langid = {german},
  pagetotal = {115},
  type = {Master's thesis}
}

@inproceedings{bleckerProductConfigurationSystems2004,
  title = {Product {{Configuration Systems}} - {{State}} of the {{Art}} - {{Conceptualization}} and {{Extensions}}},
  booktitle = {Eight {{Maghrebian Conference}} on {{Software Engineering}} and {{Artificial Intelligence}}},
  author = {Blecker, Thorsten and Abdelkafi, Nizar and Kreutler, Gerold and Friedrich, Gerhard},
  date = {2004},
  pages = {25--36},
  location = {{Sousse, Tunisia}},
  abstract = {Product configurators are considered to be among the most successful applications of artificial intelligence technology. In this paper, we determine different conceptualizations of configurators and condense them in a comprehensive morphological box, which should support configurator designers as well as decision makers in selecting the right system. The analysis of the criteria according to which configurators that are designed thus far reveals a neglect of the front-end perspective. Therefore, it is relevant to extend configurators with a front-end component assisting customers during product configuration through advisory. We develop a framework describing the main requirements on an advisory system and propose the technical infrastructure for its implementation. Finally, the advisory system and the configurator are integrated into a comprehensive interaction system.},
  eventtitle = {{{MCSEAI}} 2004},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\XVBC4BP8\\Blecker et al. - Product Configuration Systems - State of the Art -.pdf},
  langid = {english}
}

@article{bonnerEffectsMemberExpertise2002,
  title = {The Effects of Member Expertise on Group Decision-Making and Performance},
  author = {Bonner, Bryan L and Baumann, Michael R and Dalal, Reeshad S},
  date = {2002-07},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  shortjournal = {Organizational Behavior and Human Decision Processes},
  volume = {88},
  pages = {719--736},
  issn = {07495978},
  doi = {10.1016/S0749-5978(02)00010-9},
  abstract = {This study assesses the effects of member expertise on group decision-making and group performance. Three-person cooperative groups and three independent individuals solved either an easy or moderately difficult version of the deductive logic game Mastermind. Experimental groups were given veridical performance information, i.e., the members' rankings on prior individual administrations of the task. Control groups were not provided with this information. Results supported the predictions of this study: (1) groups gave more weight to the input of their highest performing members with the group decision-making process being best approximated by post hoc ``expert weighted'' social decision schemes and (2) groups performed at the level of the best of an equivalent number of individuals. \'O 2002 Elsevier Science (USA). All rights reserved.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\EQHEQVEN\\Bonner et al. - 2002 - The effects of member expertise on group decision-.pdf},
  langid = {english},
  number = {2}
}

@article{brodbeckDisseminationCriticalUnshared2002,
  title = {The Dissemination of Critical, Unshared Information in Decision-Making Groups: The Effects of Pre-Discussion Dissent},
  shorttitle = {The Dissemination of Critical, Unshared Information in Decision-Making Groups},
  author = {Brodbeck, Felix C. and Kerschreiter, Rudolf and Mojzisch, Andreas and Frey, Dieter and Schulz-Hardt, Stefan},
  date = {2002},
  journaltitle = {European Journal of Social Psychology},
  volume = {32},
  pages = {35--56},
  issn = {1099-0992},
  doi = {10.1002/ejsp.74},
  abstract = {Previous research in group decision making has found that in situations of a hidden profile (i.e. the best choice alternative is hidden from individual members as they consider their pre-discussion information), unshared information is disproportionately neglected and sub-optimal group choices are highly likely. In an experimental study, three-person groups decided which of three candidates to select for a professorial appointment. We hypothesised that minority dissent in pre-discussion preferences improves the consideration of unshared information in groups and increases the discovery rate of hidden profiles. As predicted, consideration of unshared information increased with minority dissent. The expectation of an improvement of group decision quality was partially supported. In diversity groups (i.e. each member prefers a different alternative) consideration of unshared information and group decision quality was significantly higher than in simple minority groups. Results are discussed in the light of theories of minority influence. The benefits of using the hidden profile paradigm with minority and diversity groups for theory development in the area of group decision making are highlighted. Copyright \textcopyright{} 2002 John Wiley \& Sons, Ltd.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\DTVUVFD5\\ejsp.html},
  langid = {english},
  number = {1}
}

@article{burkeHybridRecommenderSystems2002,
  title = {Hybrid {{Recommender Systems}}: {{Survey}} and {{Experiments}}},
  author = {Burke, Robin},
  date = {2002},
  journaltitle = {User modeling and user-adapted interaction},
  volume = {12},
  pages = {331--370},
  abstract = {Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative \textcent{}ltering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative \textcent{}ltering.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\7DKPN9IX\\Burke - Hybrid Recommender Systems Survey and Experiments.pdf},
  langid = {english},
  number = {4}
}

@article{cachedaComparisonCollaborativeFiltering2011,
  title = {Comparison of Collaborative Filtering Algorithms: {{Limitations}} of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems},
  shorttitle = {Comparison of Collaborative Filtering Algorithms},
  author = {Cacheda, Fidel and Carneiro, V\'ictor and Fern\'andez, Diego and Formoso, Vreixo},
  date = {2011-02-01},
  journaltitle = {ACM Transactions on the Web},
  shortjournal = {ACM Trans. Web},
  volume = {5},
  pages = {1--33},
  issn = {15591131},
  doi = {10.1145/1921591.1921593},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9W6LI9NE\\Cacheda et al. - 2011 - Comparison of collaborative filtering algorithms .pdf},
  langid = {english},
  number = {1}
}

@article{carboneIndividualVsGroup2019,
  title = {Individual vs. Group Decision-Making: An Experiment on Dynamic Choice under Risk and Ambiguity},
  shorttitle = {Individual vs. Group Decision-Making},
  author = {Carbone, Enrica and Georgalos, Konstantinos and Infante, Gerardo},
  date = {2019-07},
  journaltitle = {Theory and Decision},
  shortjournal = {Theory Decis},
  volume = {87},
  pages = {87--122},
  issn = {0040-5833, 1573-7187},
  doi = {10.1007/s11238-019-09694-8},
  abstract = {This paper focuses on the comparison of individual and group decision making, in a stochastic inter-temporal problem in two decision environments, namely risk and ambiguity. Using a consumption/saving laboratory experiment, we investigate behaviour in four treatments: (1) individual choice under risk; (2) group choice under risk; (3) individual choice under ambiguity and (4) group choice under ambiguity. Comparing decisions within and between decision environments, we find an anti-symmetric pattern. While individuals are choosing on average closer to the theoretical optimal predictions, compared to groups in the risk treatments, groups tend to deviate less under ambiguity. Within decision environments, individuals deviate more when they choose under ambiguity, while groups are better planners under ambiguity rather than under risk. Our results extend the often observed pattern of individuals (groups) behaving more optimally under risk (ambiguity), to its dynamic dimension.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\QMM4YRBJ\\Carbone et al. - 2019 - Individual vs. group decision-making an experimen.pdf},
  langid = {english},
  number = {1}
}

@online{CASSoftwareAG,
  title = {{{CAS Software AG}}},
  url = {https://www.cas.de/en/company/cas-software-ag.html},
  urldate = {2019-11-13},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\2JDND869\\cas-software-ag.html},
  type = {Company Website}
}

@online{CASSoftwareAGa,
  title = {{{CAS Software AG}} | {{CAS Software AG}}},
  url = {https://www.cas.de/https://www.cas.de/en/company/cas-software-ag.html},
  urldate = {2020-04-27},
  abstract = {\{\$og.description\}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\VJW2SBRW\\cas-software-ag.html}
}

@article{charnessGroupsMakeBetter2012,
  title = {Groups {{Make Better Self}}-{{Interested Decisions}}},
  author = {Charness, Gary and Sutter, Matthias},
  date = {2012-08},
  journaltitle = {Journal of Economic Perspectives},
  shortjournal = {Journal of Economic Perspectives},
  volume = {26},
  pages = {157--176},
  issn = {0895-3309},
  doi = {10.1257/jep.26.3.157},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\43PXHXSK\\Charness und Sutter - 2012 - Groups Make Better Self-Interested Decisions.pdf},
  keywords = {group-decisions},
  langid = {english},
  number = {3}
}

@article{charnessSilenceGoldenTeam2019,
  title = {Silence Is Golden: Team Problem Solving and Communication Costs},
  shorttitle = {Silence Is Golden},
  author = {Charness, Gary and Cooper, David J. and Grossman, Zachary},
  date = {2019-10-19},
  journaltitle = {Experimental Economics},
  shortjournal = {Exp Econ},
  issn = {1386-4157, 1573-6938},
  doi = {10.1007/s10683-019-09627-w},
  abstract = {We conduct experiments comparing the performance of individuals and teams of four subjects in solving two rather different tasks. The first involves nonograms (numerical logic puzzle). Here the solution requires a series of incremental steps. The second task uses CRT-type questions, which require a single, specific insight. Contrary to the existing literature, team performance in both tasks is statistically indistinguishable from that of individuals when there is no cost to sending a message. If a tiny message cost is imposed, team performance improves and becomes statistically better than that of individuals, although still worse than previous research on teams would have suggested. Message costs reduce the quantity of messages but increase the quality, specifically the mix of good and bad suggestions. The improved quality of communication with message costs allows teams to outperform individuals.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\L24AG7GK\\Charness et al. - 2019 - Silence is golden team problem solving and commun.pdf},
  langid = {english}
}

@inproceedings{chenEmpatheticonsDesigningEmotion2014,
  title = {Empatheticons: {{Designing Emotion Awareness Tools}} for {{Group Recommenders}}},
  shorttitle = {Empatheticons},
  booktitle = {Proceedings of the {{XV International Conference}} on {{Human Computer Interaction}} - {{Interacci\'on}} '14},
  author = {Chen, Yu and Ma, Xiaojuan and Cerezo, Alfredo and Pu, Pearl},
  date = {2014},
  pages = {1--8},
  publisher = {{ACM Press}},
  location = {{Puerto de la Cruz, Tenerife, Spain}},
  doi = {10.1145/2662253.2662269},
  abstract = {Group recommender systems help users to find items of interest collaboratively. Support for such collaboration has been mainly provided by tools that visualize membership awareness, preference awareness and decision awareness. However, these mechanisms do not address group dynamic issues: how member may affect each other. In this paper, we investigate the roles of emotion awareness tools and how they may enable positive group dynamics. We first describe the design process behind a set of dynamic emoticons, which we call empatheticons. We then show that they allow users to represent, annotate, and visualize group members' emotions in GroupFun, a group music recommender. An in-depth user study (N = 18) with GroupFun demonstrates that users' emotion annotation for recommended songs can be influenced by other group members. Most importantly, empatheticons enhance users' perceptions of the connectedness (immediacy) and familiarity (intimacy) with each other and the positive group dynamics.},
  eventtitle = {The {{XV International Conference}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5U62TD2Q\\Chen et al. - 2014 - Empatheticons Designing Emotion Awareness Tools f.pdf},
  isbn = {978-1-4503-2880-7},
  langid = {english}
}

@inproceedings{chenInterfaceInteractionDesign2011,
  title = {Interface and Interaction Design for Group and Social Recommender Systems},
  booktitle = {Proceedings of the Fifth {{ACM}} Conference on {{Recommender}} Systems - {{RecSys}} '11},
  author = {Chen, Yu},
  date = {2011},
  pages = {363},
  publisher = {{ACM Press}},
  location = {{Chicago, Illinois, USA}},
  doi = {10.1145/2043932.2044007},
  abstract = {Group and social recommender systems aim to recommend items of interest to a group or a community of people. The user issues in such systems cannot be addressed by examining the satisfaction of their members as individuals. Rather, group satisfaction should be studied as a result of the interaction and interface methods that support group dynamics and interaction. In this paper, we survey the state-of-the-art in user experience design of group and social recommender systems. We further apply the techniques used in the current recommender systems to GroupFun, a music social group recommender system. After presenting the interface and interaction characteristics of GroupFun, we further analyze the design space and propose areas for future research in pursuit of an affective recommender.},
  eventtitle = {The Fifth {{ACM}} Conference},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\BXIV2L5C\\Chen - 2011 - Interface and interaction design for group and soc.pdf},
  isbn = {978-1-4503-0683-6},
  langid = {english}
}

@incollection{choudharyMulticriteriaGroupRecommender2020,
  ids = {choudharyMulticriteriaGroupRecommender2020},
  title = {Multi-Criteria {{Group Recommender System Based}} on {{Analytical Hierarchy Process}}},
  booktitle = {Smart {{Systems}} and {{IoT}}: {{Innovations}} in {{Computing}}},
  author = {Choudhary, Nirmal and Bharadwaj, K. K.},
  editor = {Somani, Arun K. and Shekhawat, Rajveer Singh and Mundra, Ankit and Srivastava, Sumit and Verma, Vivek Kumar},
  date = {2020},
  volume = {141},
  pages = {75--84},
  publisher = {{Springer Singapore}},
  location = {{Singapore}},
  doi = {10.1007/978-981-13-8406-6_8},
  abstract = {Current researches have demonstrated that the significance of MultiCriteria Decision-Making (MCDM) methods in Group Recommender Systems (GRSs) has yet to be thoroughly discovered. Thus, we have proposed a Multi-criteria GRS (MCGRS) to provide recommendations for group of users based on multicriteria optimization. The idea behind our approach is that, each member in a group have different opinions about each criterion and he/she would try to make the best use of multi-criteria to fulfill his/her own preference in decision-making process. Therefore, we have employed Analytical Hierarchy Process (AHP) to learn the priority of each criterion to maximize the utility for each criterion. Then, MCGRS generate the most appropriate recommendation for the group. Experiments are performed on Yahoo! Movies dataset and the results of comparative analysis of proposed MCGRS with baseline GRSs techniques clearly demonstrate the supremacy of our proposed model.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\EFVTG9VE\\Choudhary_Bharadwaj_2020_Multi-criteria Group Recommender System Based on Analytical Hierarchy Process.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NN5RCJY2\\Choudhary und Bharadwaj - 2020 - Multi-criteria Group Recommender System Based on A.pdf},
  isbn = {9789811384059 9789811384066},
  keywords = {Analytical hierarchy process,Decision-making,Multi-criteria group recommender systems,Recommendation mechanism},
  langid = {english}
}

@article{chrysanthis1998recovery,
  title = {Recovery and Performance of Atomic Commit Processing in Distributed Database Systems},
  author = {Chrysanthis, PK and Samaras, G and Al-Houmaily, YJ},
  date = {1998},
  journaltitle = {Recovery Mechanisms in Database Systems},
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@online{ConfigurationSolutions,
  title = {Configuration {{Solutions}}},
  journaltitle = {CAS Merlin},
  url = {https://www.cas-merlin.com/configuration-solutions.html},
  urldate = {2020-04-27},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\IC3W4XAG\\configuration-solutions.html}
}

@online{cookACIDBASEDatabase2009,
  title = {{{ACID}} versus {{BASE}} for Database Transactions},
  author = {Cook, John D.},
  date = {2009-07-06},
  journaltitle = {John D. Cook Consulting},
  url = {https://www.johndcook.com/blog/2009/07/06/brewer-cap-theorem-base/},
  urldate = {2020-04-27},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NEN4DCZV\\brewer-cap-theorem-base.html}
}

@inproceedings{cosley2003seeing,
  title = {Is Seeing Believing?: How Recommender System Interfaces Affect Users' Opinions},
  booktitle = {Proceedings of the {{SIGCHI}} Conference on {{Human}} Factors in Computing Systems},
  author = {Cosley, Dan and Lam, Shyong K and Albert, Istvan and Konstan, Joseph A and Riedl, John},
  date = {2003},
  pages = {585--592},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\X5WCB36N\\Cosley et al_2003_Is seeing believing.pdf},
  organization = {{ACM}}
}

@article{costerEnhancingWebbasedConfiguration,
  title = {Enhancing Web-Based Configuration with Recommendations and Cluster-Based Help},
  author = {Coster, Rickard and Gustavsson, Andreas and Olsson, Tomas},
  pages = {10},
  abstract = {In a collaborative project with Tacton AB, we have investigated new ways of assisting the user in the process of on-line product configuration. A web-based prototype, RIND, was built for ephemeral users in the domain of PC configuration.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5VQLH7YB\\Coster et al. - Enhancing web-based conﬁguration with recommendati.pdf},
  langid = {english}
}

@article{costerEnhancingWebbasedConfigurationa,
  title = {Enhancing Web-Based Configuration with Recommendations and Cluster-Based Help},
  author = {Coster, Rickard and Gustavsson, Andreas and Olsson, Tomas},
  pages = {10},
  abstract = {In a collaborative project with Tacton AB, we have investigated new ways of assisting the user in the process of on-line product configuration. A web-based prototype, RIND, was built for ephemeral users in the domain of PC configuration.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\WGJLRB9Q\\Coster et al. - Enhancing web-based conﬁguration with recommendati.pdf},
  langid = {english}
}

@article{crottGroupDecisionChoice1991,
  title = {Group Decision, Choice Shift, and Polarization in Consulting, Political, and Local Political Scenarios: {{An}} Experimental Investigation and Theoretical Analysis},
  shorttitle = {Group Decision, Choice Shift, and Polarization in Consulting, Political, and Local Political Scenarios},
  author = {Crott, Helmut W and Szilvas, Klaus and Zuber, Johannes A},
  date = {1991-06},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  shortjournal = {Organizational Behavior and Human Decision Processes},
  volume = {49},
  pages = {22--41},
  issn = {07495978},
  doi = {10.1016/0749-5978(91)90040-Z},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\CXGJWE27\\Crott et al. - 1991 - Group decision, choice shift, and polarization in .pdf},
  langid = {english},
  number = {1}
}

@inproceedings{de2002importance,
  title = {On the Importance of the Separation-of-Concerns Principle in Secure Software Engineering},
  booktitle = {Workshop on the Application of Engineering Principles to System Security Design},
  author = {De Win, Bart and Piessens, Frank and Joosen, Wouter and Verhanneman, Tine},
  date = {2002},
  pages = {1--10},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\RKSI6M9G\\Verhanneman - On the importance of the separation-of-concerns pr.pdf}
}

@inproceedings{delgadoSimpleObjectivesWork2019,
  title = {Simple {{Objectives Work Better}}},
  booktitle = {Proceedings of the {{Workshop}} on {{Recommendation}} in {{Multi}}-Stakeholder {{Environments}}},
  author = {Delgado, Joaquin and Lind, Samuel and Radecke, Carl and Konijeti, Satish},
  date = {2019-09-20},
  location = {{Copenhagen, Denmark}},
  eventtitle = {{{RMSE}} 2019},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\6CPFZC8M\\Delgado et al_2019_Simple Objectives Work Better.pdf}
}

@article{delicResearchMethodsGroup2016,
  title = {Research {{Methods}} for {{Group Recommender Systems}}},
  author = {Delic, Amra and Neidhardt, Julia and Nguyen, Thuy Ngoc},
  date = {2016},
  pages = {8},
  abstract = {In this article we argue that the research on group recommender systems must look more carefully at group dynamics in decision making in order to produce technologies that will be truly beneficial for users. Hence, we illustrate a user study method aimed at observing and measuring the evolution of user preferences and actions in a tourism decision making task: finding a destination to visit. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings may have on the design of interactive group recommender systems.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\IXW2MLZP\\Delic et al. - Research Methods for Group Recommender Systems.pdf},
  keywords = {group recommender,methods},
  langid = {english}
}

@article{dewinImportanceSeparationofconcernsPrinciple2002,
  title = {On the Importance of the Separation-of-Concerns Principle in Secure Software Engineering},
  author = {De Win, Bart and Piessens, Frank and Joosen, Wouter and Verhanneman, Tine},
  date = {2002-09-30},
  pages = {10},
  abstract = {The separation-of-concerns principle is one of the essential principles in software engineering. It says that software should be decomposed in such a way that different ``concerns'' or aspects of the problem at hand are solved in well-separated modules or parts of the software. Yet, many security experts feel uneasy about trying to isolate security-related concerns, because security is such a pervasive property of a piece of software. And in fact, separating security-related concerns such as access control, or defensive input checking, is indeed very hard to achieve with current software engineering techniques.},
  langid = {english}
}

@inproceedings{dingContextAwareRecommender2018,
  title = {Context {{Aware Recommender System}} for {{Large Scaled Flash Sale Sites}}},
  booktitle = {2018 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})},
  author = {Ding, Wanying and Xu, Ran and Ding, Ying and Zhang, Yue and Luo, Chuanjiang and Yu, Zhendong},
  date = {2018-12},
  pages = {993--1000},
  publisher = {{IEEE}},
  location = {{Seattle, WA, USA}},
  doi = {10.1109/BigData.2018.8622062},
  abstract = {Flash Sale Sites popularize because they save great money for users. Good recommender systems can further save users' time to improve their online shopping experiences. Although there exsit a lot of studies on recommender system, very few focus on flash sale sites. Big Data, Context Sensitivity, and Feature Engineering are three key challenges for one to build a good recommender system. This paper proposes two deep learning oriented models: Tensor-AutoRec and HybridAutoRec to cope with the problems within an industrial context. First, these two models can handle storage and speed problem caused by big data. Second, both models incorporate context information, so they can generate more relevant recommendations by adapting to specific contextual situations. Third, our deep learning-based models can be trained end-to-end without tedious feature engineerings. Extensive experiments with a half year real transcation data demonstrate that our models can outperform classifcal ones in terms of different evaluation metrices. Finally, online A/B testing results showed that our model can improve our old recommendation system over various online performance indicators.},
  eventtitle = {2018 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\HMKXV8NX\\Ding et al. - 2018 - Context Aware Recommender System for Large Scaled .pdf},
  isbn = {978-1-5386-5035-6},
  langid = {english}
}

@article{elahiSurveyActiveLearning2016,
  title = {A Survey of Active Learning in Collaborative Filtering Recommender Systems},
  author = {Elahi, Mehdi and Ricci, Francesco and Rubens, Neil},
  date = {2016-05},
  journaltitle = {Computer Science Review},
  shortjournal = {Computer Science Review},
  volume = {20},
  pages = {29--50},
  issn = {15740137},
  doi = {10.1016/j.cosrev.2016.05.002},
  abstract = {In collaborative filtering recommender systems user's preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system's recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user's tastes. Hence, specific techniques, which are defined as ``active learning strategies'', can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users' preferences and enables to generate better recommendations.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\4IR8QEFN\\Elahi et al. - 2016 - A survey of active learning in collaborative filte.pdf},
  langid = {english},
  note = {ZSCC: 0000118}
}

@article{esserAliveWell251998,
  title = {Alive and {{Well}} after 25 {{Years}}: {{A Review}} of {{Groupthink Research}}},
  shorttitle = {Alive and {{Well}} after 25 {{Years}}},
  author = {Esser, James K},
  date = {1998-02-01},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  shortjournal = {Organizational Behavior and Human Decision Processes},
  volume = {73},
  pages = {116--141},
  issn = {0749-5978},
  doi = {10.1006/obhd.1998.2758},
  abstract = {This article provides a summary of empirical research on groupthink theory. Groupthink research, including analyses of historical cases of poor group decision making and laboratory tests of groupthink, is reviewed. Results from these two research areas are briefly compared. Theoretical and methodological issues for future groupthink research are identified and discussed. I conclude that groupthink research has had and continues to have considerable heuristic value. A small, but growing, body of empirical literature has been generated. In addition, groupthink research has stimulated a number of theoretical ideas, most of which have yet to be tested.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\KMR4CLGA\\Esser_1998_Alive and Well after 25 Years.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NSYG2B7Z\\S0749597898927583.html},
  keywords = {group-decisions,groupthink},
  langid = {english},
  number = {2}
}

@article{falknerRecommendationTechnologiesConfigurable2011,
  title = {Recommendation {{Technologies}} for {{Configurable Products}}},
  author = {Falkner, Andreas and Felfernig, Alexander and Haag, Albert},
  date = {2011-10-31},
  journaltitle = {AI Magazine},
  shortjournal = {AIMag},
  volume = {32},
  pages = {99},
  issn = {0738-4602, 0738-4602},
  doi = {10.1609/aimag.v32i3.2369},
  abstract = {State of the art recommender systems support users in the selection of items from a predefined assortment (e.g., movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are represented in the form of a configuration knowledge base that describes the properties of allowed instances. Although the knowledge representation used is different compared to non-configurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this paper we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\4QVSX5DQ\\Falkner et al. - 2011 - Recommendation Technologies for Configurable Produ.pdf},
  langid = {english},
  number = {3}
}

@online{FeaturebasedPersonalizedRecommender,
  title = {A Feature-Based Personalized Recommender System for Product-Line Configuration | {{Proceedings}} of the 2016 {{ACM SIGPLAN International Conference}} on {{Generative Programming}}: {{Concepts}} and {{Experiences}}},
  shorttitle = {A Feature-Based Personalized Recommender System for Product-Line Configuration | {{Proceedings}} of the 2016 {{ACM SIGPLAN International Conference}} on {{Generative Programming}}},
  url = {https://dl.acm.org/doi/abs/10.1145/2993236.2993249},
  urldate = {2020-01-15},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\WIYHMJRF\\2993236.html},
  langid = {english}
}

@incollection{felfernigAlgorithmsGroupRecommendation2018,
  title = {Algorithms for {{Group Recommendation}}},
  booktitle = {Group {{Recommender Systems}} : {{An Introduction}}},
  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  editor = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  date = {2018},
  pages = {27--58},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-75067-5_2},
  abstract = {In this chapter, our aim is to show how group recommendation can be implemented on the basis of recommendation paradigms for individual users. Specifically, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) aggregated predictions and (2) aggregated models as basic strategies for aggregating the preferences of individual group members.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\4JL9AY5X\\Felfernig et al_2018_Algorithms for Group Recommendation.pdf},
  isbn = {978-3-319-75067-5},
  langid = {english},
  series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}}
}

@incollection{felfernigBiasesGroupDecisions2018,
  title = {Biases in {{Group Decisions}}},
  booktitle = {Group {{Recommender Systems}} : {{An Introduction}}},
  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  editor = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  date = {2018},
  pages = {145--155},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-75067-5_8},
  abstract = {Decision biases can be interpreted as tendencies to think and act in specific ways that result in a systematic deviation of potentially rational and high-quality decisions. In this chapter, we provide an overview of example decision biases and show possibilities to counteract these. The overview includes (1) biases that exist in both single user and group decision making (decoy effects, serial position effects, framing, and anchoring) and (2) biases that especially occur in the context of group decision making (GroupThink, polarization, and emotional contagion).},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NPY64XZ8\\Felfernig et al_2018_Biases in Group Decisions.pdf},
  isbn = {978-3-319-75067-5},
  langid = {english},
  series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}}
}

@inproceedings{felfernigConstraintbasedRecommenderSystems2008,
  title = {Constraint-Based {{Recommender Systems}}: {{Technologies}} and {{Research Issues}}},
  booktitle = {Proceedings of the 10th International Conference on {{Electronic}} Commerce},
  author = {Felfernig, A and Burke, R},
  date = {2008},
  pages = {3},
  abstract = {Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\BBYQH8IW\\Felfernig und Burke - Constraint-based Recommender Systems Technologies.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\N6WZY4DL\\AConfiguration-basedRecommenderSystemForSupportingE-commerceDecisions.pdf},
  langid = {english}
}

@incollection{felfernigDecisionTasksBasic2018,
  title = {Decision {{Tasks}} and {{Basic Algorithms}}},
  booktitle = {Group {{Recommender Systems}} : {{An Introduction}}},
  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  editor = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  date = {2018},
  pages = {3--26},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-75067-5_1},
  abstract = {Recommender systems are decision support systems helping users to identify one or more items (solutions) that fit their wishes and needs. The most frequent application of recommender systems nowadays is to propose items to individual users. However, there are many scenarios where a group of users should receive a recommendation. For example, think of a group decision regarding the next holiday destination or a group decision regarding a restaurant to visit for a joint dinner. The goal of this book is to provide an introduction to group recommender systems, i.e., recommender systems that determine recommendations for groups. In this chapter, we provide an introduction to basic types of recommendation algorithms for individual users and characterize related decision tasks. This introduction serves as a basis for the introduction of group recommendation algorithms in Chap. 2.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\745225S2\\Felfernig et al_2018_Decision Tasks and Basic Algorithms.pdf},
  isbn = {978-3-319-75067-5},
  langid = {english},
  series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}}
}

@inproceedings{felfernigGroupDecisionSupport2011,
  title = {Group {{Decision Support}} for {{Requirements Negotiation}}},
  author = {Felfernig, Alexander and Zehentner, Christoph and Ninaus, Gerald and Grabner, Harald and Maalej, Walid and Pagano, Dennis and Weninger, Leopold and Reinfrank, Florian},
  date = {2011},
  pages = {105--116},
  abstract = {Requirements engineering is one of the most critical phases in software development. Requirements verbalize decision alternatives that are negotiated by stakeholders. In this paper we present the results of an empirical analysis of the effects of applying group recommendation technologies to requirements negotiation. This analysis has been conducted within the scope of software development projects at our university where development teams were supported with group recommendation technologies when deciding which requirements should be implemented. A major result of the study is that group recommendation technologies can improve the perceived usability (in certain cases) and the perceived quality of decision support. Furthermore, it is not recommended to disclose preferences of individual group members at the beginning of a decision process \textendash{} this could lead to an insufficient exchange of decision-relevant information.},
  eventtitle = {International {{Conference}} on {{User Modeling}}, {{Adaptation}}, and {{Personalization}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\H3ESBD4X\\Felfernig et al. - 2011 - Group Decision Support for Requirements Negotiatio.pdf},
  langid = {english}
}

@incollection{felfernigGroupRecommenderApplications2018,
  title = {Group {{Recommender Applications}}},
  booktitle = {Group {{Recommender Systems}} : {{An Introduction}}},
  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  editor = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  date = {2018},
  pages = {75--89},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-75067-5_4},
  abstract = {In this chapter, we present an overview of different group recommender applications. We organize this overview into the application domains of music, movies and TV programs, travel destinations and events, news and web pages, healthy living, software engineering, and domain-independent recommenders. Each application is analyzed with regard to the characteristics of group recommenders as introduced in Chap. 2.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\49BRYWWD\\Felfernig et al_2018_Group Recommender Applications.pdf},
  isbn = {978-3-319-75067-5},
  langid = {english},
  series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}}
}

@book{felfernigGroupRecommenderSystems2018,
  title = {Group {{Recommender Systems An Introduction}}},
  shorttitle = {Group Recommender Systems},
  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal\v{c}i\v{c}, Marko},
  date = {2018},
  abstract = {This book presents group recommender systems, which focus on the determination of recommendations for groups of users. The authors summarize different technologies and applications of group recommender systems. They include an in-depth discussion of state-of-the-art algorithms, an overview of industrial applications, an inclusion of the aspects of decision biases in groups, and corresponding de-biasing approaches. The book includes a discussion of basic group recommendation methods, aspects of human decision making in groups, and related applications. A discussion of open research issues is included to inspire new related research. The book serves as a reference for researchers and practitioners working on group recommendation related topics.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\HC6C7C89\\Felfernig et al. - 2018 - Group recommender systems an introduction.pdf},
  isbn = {978-3-319-75067-5},
  langid = {english}
}

@collection{felfernigKnowledgebasedConfigurationResearch2014,
  title = {Knowledge-Based Configuration: From Research to Business Cases},
  shorttitle = {Knowledge-Based Configuration},
  editor = {Felfernig, Alexander and Hotz, Lothar and Bagley, Claire and Tiihonen, Juha},
  date = {2014},
  publisher = {{Elsevier/MK, Morgan Kaufmann}},
  location = {{Amsterdam}},
  isbn = {978-0-12-415817-7},
  langid = {english},
  note = {OCLC: 915548311}
}

@inproceedings{felfernigOpenConfiguration2014,
  title = {Towards {{Open Configuration}}},
  author = {Felfernig, Alexander and Stettinger, Martin and Ninaus, Gerald and Jeran, Michael and Reiterer, Stefan and Falkner, Andreas and Leitner, Gerhard and Tiihonen, Juha},
  date = {2014},
  pages = {89--94},
  abstract = {Configuration technologies are typically applied in closed settings where one (or a small group of) knowledge engineer(s) is in charge of knowledge base development and maintenance. In such settings it is also assumed that only single users configure the corresponding products and services. Nowadays, a couple of scenarios exist that require more openness: it should be possible to cooperatively develop knowledge bases and to jointly configure products and services, even by adding new features or constraints in a flexible fashion. We denote this integration of groups of users into configuration-related tasks as open configuration. In this paper we introduce features of open configuration environments and potential approaches to implement these features.},
  eventtitle = {Configuration {{Workshop}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\KA3YWQCN\\Felfernig et al. - 2014 - Towards Open Conﬁguration.pdf},
  langid = {english}
}

@article{felfernigOpenConfiguration2014a,
  title = {Towards {{Open Configuration}}},
  author = {Felfernig, Alexander and Stettinger, Martin and Ninaus, Gerald and Jeran, Michael and Reiterer, Stefan and Falkner, Andreas and Leitner, Gerhard and Tiihonen, Juha},
  date = {2014},
  url = {https://researchportal.helsinki.fi/en/publications/towards-open-configuration},
  urldate = {2019-10-15},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NAFRZ496\\towards-open-configuration.html},
  langid = {english}
}

@inproceedings{felfernigPersonalizedUserInterfaces2010,
  title = {Personalized User Interfaces for Product Configuration},
  booktitle = {Proceedings of the 15th International Conference on {{Intelligent}} User Interfaces},
  author = {Felfernig, Alexander and Mandl, Monika and Tiihonen, Juha and Schubert, Monika and Leitner, Gerhard},
  date = {2010-02-07},
  pages = {317--320},
  publisher = {{Association for Computing Machinery}},
  location = {{Hong Kong, China}},
  doi = {10.1145/1719970.1720020},
  abstract = {Configuration technologies are well established as a foundation of mass customization which is a production paradigm that supports the manufacturing of highly-variant products under pricing conditions similar to mass production. A side-effect of the high diversity of products offered by a configurator is that the complexity of the alternatives may outstrip a user's capability to explore them and make a buying decision. In order to improve the quality of configuration processes, we combine knowledge-based configuration with collaborative and content-based recommendation algorithms. In this paper we present configuration techniques that recommend personalized default values to users. Results of an empirical study show improvements in terms of, for example, user satisfaction or the quality of the configuration process.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FRT6HPDN\\Felfernig et al. - 2010 - Personalized user interfaces for product configura.pdf},
  isbn = {978-1-60558-515-4},
  keywords = {configuration systems,model-based diagnosis,recommender systems},
  series = {{{IUI}} '10}
}

@inproceedings{felfernigPersuasiveRecommendationSerial2007,
  title = {Persuasive {{Recommendation}}: {{Serial Position Effects}} in {{Knowledge}}-{{Based Recommender Systems}}},
  shorttitle = {Persuasive {{Recommendation}}},
  booktitle = {Persuasive {{Technology}}},
  author = {Felfernig, A. and Friedrich, G. and Gula, B. and Hitz, M. and Kruggel, T. and Leitner, G. and Melcher, R. and Riepan, D. and Strauss, S. and Teppan, E. and Vitouch, O.},
  editor = {de Kort, Yvonne and IJsselsteijn, Wijnand and Midden, Cees and Eggen, Berry and Fogg, B. J.},
  date = {2007},
  pages = {283--294},
  publisher = {{Springer}},
  location = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-540-77006-0_34},
  abstract = {Recommender technologies are crucial for the effective support of customers in online sales situations. The state-of-the-art research in recommender systems is not aware of existing theories in the areas of cognitive and decision psychology and thus lacks of deeper understanding of online buying situations. In this paper we present results from user studies related to serial position effects in human memory in the context of knowledge-based recommender applications. We discuss serial position effects on the recall of product descriptions as well as on the probability of product selection. Serial position effects such as primacy and recency are major building blocks of persuasive, next generation knowledge-based recommender systems.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\T38JCWRX\\Felfernig et al_2007_Persuasive Recommendation.pdf},
  isbn = {978-3-540-77006-0},
  keywords = {human memory,interactive selling,knowledge-based recommendation,persuasive technologies,recommender systems},
  langid = {english},
  options = {useprefix=true},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@inproceedings{felfernigPersuasiveRecommendationSerial2007a,
  title = {Persuasive {{Recommendation}}: {{Serial Position Effects}} in {{Knowledge}}-{{Based Recommender Systems}}},
  shorttitle = {Persuasive {{Recommendation}}},
  booktitle = {Persuasive {{Technology}}},
  author = {Felfernig, A. and Friedrich, G. and Gula, B. and Hitz, M. and Kruggel, T. and Leitner, G. and Melcher, R. and Riepan, D. and Strauss, S. and Teppan, E. and Vitouch, O.},
  editor = {de Kort, Yvonne and IJsselsteijn, Wijnand and Midden, Cees and Eggen, Berry and Fogg, B. J.},
  date = {2007},
  pages = {283--294},
  publisher = {{Springer}},
  location = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-540-77006-0_34},
  abstract = {Recommender technologies are crucial for the effective support of customers in online sales situations. The state-of-the-art research in recommender systems is not aware of existing theories in the areas of cognitive and decision psychology and thus lacks of deeper understanding of online buying situations. In this paper we present results from user studies related to serial position effects in human memory in the context of knowledge-based recommender applications. We discuss serial position effects on the recall of product descriptions as well as on the probability of product selection. Serial position effects such as primacy and recency are major building blocks of persuasive, next generation knowledge-based recommender systems.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FK4JBI59\\Felfernig et al_2007_Persuasive Recommendation.pdf},
  isbn = {978-3-540-77006-0},
  keywords = {human memory,interactive selling,knowledge-based recommendation,persuasive technologies,recommender systems},
  langid = {english},
  options = {useprefix=true},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@inproceedings{felfernigProceedings20thInternational,
  title = {Proceedings of the 20th {{International Configuration Workshop}}},
  author = {Felfernig, Alexander and Tiihonen, Juha and Hotz, Lothar and Stettinger, Martin},
  pages = {132},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FW39YC58\\Felfernig et al. - University of Hamburg Hamburger Informatik Technol.pdf},
  langid = {english}
}

@inproceedings{felferningGroupBasedConfiguration2016,
  title = {Towards {{Group}}-{{Based Configuration}}},
  shorttitle = {18th {{International}} Configuration Workshop},
  author = {Felferning, Alexander and Atas, M\"usl\"um and Tran, Thi Ngoc Trang and Stettinger, Martin},
  date = {2016},
  pages = {69--72},
  publisher = {{\'Ecole des Mines d'Albi-Carmaux}},
  location = {{Albi}},
  eventtitle = {International {{Workshop}} on {{Configuration}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\YF83I4TQ\\Felferning et al. - 2016 - Towards Group-Based Configuration.pdf},
  isbn = {979-10-91526-04-3},
  langid = {english}
}

@online{FlaskDocumentation,
  title = {Flask {{Documentation}} (1.1.x)},
  url = {https://flask.palletsprojects.com/en/1.1.x/},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\IYAG7QV6\\1.1.x.html}
}

@online{FlaskRESTPlus13Documentation,
  title = {Flask-{{RESTPlus}} 0.13.0 Documentation},
  url = {https://flask-restplus.readthedocs.io/en/stable/},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\CI5RGD28\\stable.html}
}

@book{gamma2015design,
  title = {Design patterns: Entwurfsmuster als elemente wiederverwendbarer objektorientierter software},
  author = {Gamma, Erich and Helm, Richard and Johnson, Ralph and Vlissides, John},
  date = {2015},
  publisher = {{MITP-Verlags GmbH \& Co. KG}},
  langid = {german}
}

@inproceedings{garciaGroupRecommenderSystem2009,
  title = {A {{Group Recommender System}} for {{Tourist Activities}}},
  booktitle = {E-{{Commerce}} and {{Web Technologies}}},
  author = {Garcia, Inma and Sebastia, Laura and Onaindia, Eva and Guzman, Cesar},
  editor = {Di Noia, Tommaso and Buccafurri, Francesco},
  date = {2009},
  pages = {26--37},
  publisher = {{Springer}},
  location = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-03964-5_4},
  abstract = {This paper introduces a method for giving recommendations of tourist activities to a group of users. This method makes recommendations based on the group tastes, their demographic classification and the places visited by the users in former trips. The group recommendation is computed from individual personal recommendations through the use of techniques such as aggregation, intersection or incremental intersection. This method is implemented as an extension of the e-Tourism tool, which is a user-adapted tourism and leisure application, whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven search engine that manages the group recommendation.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\JDW2ZGP8\\Garcia et al_2009_A Group Recommender System for Tourist Activities.pdf},
  isbn = {978-3-642-03964-5},
  keywords = {Group Recommenders,Recommender Systems,Tourism},
  langid = {english},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@article{glogerScrumPradigmenwechselIm2010,
  title = {Scrum: Der Pradigmenwechsel im Projekt- und Produktmanagement \textendash{} Eine Einf\"uhrung},
  shorttitle = {Scrum},
  author = {Gloger, Boris},
  date = {2010-04},
  journaltitle = {Informatik-Spektrum},
  shortjournal = {Informatik Spektrum},
  volume = {33},
  pages = {195--200},
  issn = {0170-6012, 1432-122X},
  doi = {10.1007/s00287-010-0426-6},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\JQ3EDHKS\\Gloger - 2010 - Scrum Der Pradigmenwechsel im Projekt- und Produk.pdf},
  langid = {german},
  number = {2}
}

@inproceedings{grasIdentifyingGreySheep2016,
  title = {Identifying {{Grey Sheep Users}} in {{Collaborative Filtering}}: {{A Distribution}}-{{Based Technique}}},
  shorttitle = {Identifying {{Grey Sheep Users}} in {{Collaborative Filtering}}},
  booktitle = {Proceedings of the 2016 {{Conference}} on {{User Modeling Adaptation}} and {{Personalization}}},
  author = {Gras, Benjamin and Brun, Armelle and Boyer, Anne},
  date = {2016-07-13},
  pages = {17--26},
  publisher = {{Association for Computing Machinery}},
  location = {{Halifax, Nova Scotia, Canada}},
  doi = {10.1145/2930238.2930242},
  abstract = {The collaborative filtering (CF) approach in recommender systems assumes that users' preferences are consistent among users. Although accurate, this approach fails on some users. We presume that some of these users belong to a small community of users who have unusual preferences, such users are not compliant with the CF underlying assumption. They are grey sheep users. This paper aims at accurately identifying grey sheep users. We introduce a new distribution-based grey sheep users identification technique, that borrows from outlier detection and from information retrieval, while taking into account the specificities of preference data on which CF relies: extreme sparsity, imprecision and users' bias. The experimental evaluation conducted on a state-of-the-art dataset shows that this new distribution-based technique outperforms state-of-the-art grey sheep users identification techniques.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5RQPTIUR\\Gras et al_2016_Identifying Grey Sheep Users in Collaborative Filtering.pdf},
  isbn = {978-1-4503-4368-8},
  keywords = {collaborative filtering,grey sheep users,outlier detection,recommender systems},
  series = {{{UMAP}} '16}
}

@article{haagProductConfigurationDecision2011,
  title = {Product Configuration as Decision Support: {{The}} Declarative Paradigm in Practice},
  shorttitle = {Product Configuration as Decision Support},
  author = {Haag, Albert and Riemann, Steffen},
  date = {2011-05},
  journaltitle = {Artificial Intelligence for Engineering Design, Analysis and Manufacturing},
  shortjournal = {AIEDAM},
  volume = {25},
  pages = {131--142},
  issn = {0890-0604, 1469-1760},
  doi = {10.1017/S0890060410000582},
  abstract = {Product configuration is a key technology, which enables businesses to deliver and deploy individualized products. In many cases, finding the optimal configuration solution for the user is a creative process that requires them to decide trade-offs between conflicting goals (multicriteria optimization problem). These problems are best supported by an interactive dialog that is managed by a dedicated software program (the configurator) that provides decision support. We illustrate this using a real example (configuration of a business software system). This productively used application makes the user aware of which choices are available in a given situation, provides assistance in resolving inconsistent choices and defaults, and generates explanations if desired. One of the key configurator components used to manage this is a truth maintenance system. We describe how this component is used and two novel extensions to it: methods for declarative handling of defaults (of varying strength) and the declarative handling of incompleteness. Finally, we summarize our experiences made during the implementation of this application and the pros and cons of declarative versus procedural approaches.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\CCLKMSQI\\Haag and Riemann - 2011 - Product configuration as decision support The dec.pdf},
  langid = {english},
  number = {2}
}

@article{habraSoftwareEngineeringEducation,
  title = {In {{Software Engineering Education}}},
  author = {Habra, Naji},
  pages = {6},
  abstract = {Separation of concerns is the main principle of Software Engineering. It represents a key element in the teaching process of any Software Engineering methodology. The paper relates the experience of the University of Namur in introducing the separation of concerns principle in its educational scheme through an extended student project.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\MVX62LT3\\Habra - in Software Engineering Education.pdf},
  langid = {english}
}

@article{hahslerRecommenderlabFrameworkDeveloping,
  title = {Recommenderlab: {{A Framework}} for {{Developing}} and {{Testing Recommendation Algorithms}}},
  author = {Hahsler, Michael},
  pages = {40},
  abstract = {The problem of creating recommendations given a large data base from directly elicited ratings (e.g., ratings of 1 through 5 stars) is a popular research area which was lately boosted by the Netflix Prize competition. While several libraries which implement recommender algorithms have been developed over the last decade there is still the need for a framework which facilitates research on recommender systems by providing a common development and evaluation environment. This paper describes recommenderlab which provides the infrastructure to develop and test recommender algorithms for rating data and 0-1 data in a unified framework. The Package provides basic algorithms and allows the user to develop and use his/her own algorithms in the framework via a simple registration procedure.},
  langid = {english}
}

@report{hahslerRecommenderlabFrameworkDeveloping2015,
  title = {Recommenderlab: {{A Framework}} for {{Developing}} and {{Testing Recommendation Algorithms}}},
  author = {Hahsler, Michael},
  date = {2015},
  url = {http://elib.ict.nsc.ru/jspui/handle/ICT/1861},
  urldate = {2020-02-19},
  abstract = {The problem of creating recommendations given a large data base from directly elicited 
ratings (e.g., ratings of 1 through 5 stars) is a popular research area which was lately 
boosted by the Netflix Prize competition. While several libraries which implement recommender algorithms have been developed over the last decade there is still the need for 
a framework which facilitates research on recommender systems by providing a common 
development and evaluation environment. This paper describes recommenderlab which 
provides the infrastructure to develop and test recommender algorithms for rating data 
and 0-1 data in a unified framework. The Package provides basic algorithms and allows the 
user to develop and use his/her own algorithms in the framework via a simple registration 
procedure.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5T2FEWC3\\Hahsler_2015_recommenderlab.pdf}
}

@article{hernandezdelolmoEvaluationRecommenderSystems2008,
  title = {Evaluation of Recommender Systems: {{A}} New Approach},
  shorttitle = {Evaluation of Recommender Systems},
  author = {Hern\'andez del Olmo, F\'elix and Gaudioso, Elena},
  date = {2008-10},
  journaltitle = {Expert Systems with Applications},
  shortjournal = {Expert Systems with Applications},
  volume = {35},
  pages = {790--804},
  issn = {09574174},
  doi = {10.1016/j.eswa.2007.07.047},
  abstract = {It is difficult to deny that comparison between recommender systems requires a common way for evaluating them. Nevertheless, at present, they have been evaluated in many, often incompatible, ways. We affirm this problem is mainly due to the lack of a common framework for recommender systems, a framework general enough so that we may include the whole range of recommender systems to date, but specific enough so that we can obtain solid results. In this paper, we propose such a framework, attempting to extract the essential features of recommender systems. In this framework, the most essential feature is the objective of the recommender system. What is more, in this paper, recommender systems are viewed as applications with the following essential objective. Recommender systems must: (i) choose which (of the items) should be shown to the user, (ii) decide when and how the recommendations must be shown. Next, we will show that a new metric emerges naturally from this framework. Finally, we will conclude by comparing the properties of this new metric with the traditional ones. Among other things, we will show that we may evaluate the whole range of recommender systems with this single metric.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\B5DFYUDA\\Hernández del Olmo und Gaudioso - 2008 - Evaluation of recommender systems A new approach.pdf},
  langid = {english},
  number = {3}
}

@article{herrera-viedmaConsensusSupportSystem2005,
  title = {A {{Consensus Support System Model}} for {{Group Decision}}-{{Making Problems With Multigranular Linguistic Preference Relations}}},
  author = {Herrera-Viedma, E. and Martinez, L. and Mata, F. and Chiclana, F.},
  date = {2005-10},
  journaltitle = {IEEE Transactions on Fuzzy Systems},
  shortjournal = {IEEE Trans. Fuzzy Syst.},
  volume = {13},
  pages = {644--658},
  issn = {1063-6706},
  doi = {10.1109/TFUZZ.2005.856561},
  abstract = {The group decision-making framework with linguistic preference relations is studied. In this context, we assume that there exist several experts who may have different background and knowledge to solve a particular problem and, therefore, different linguistic term sets (multigranular linguistic information) could be used to express their opinions. The aim of this paper is to present a model of consensus support system to assist the experts in all phases of the consensus reaching process of group decision-making problems with multigranular linguistic preference relations. This consensus support system model is based on i) a multigranular linguistic methodology, ii) two consensus criteria, consensus degrees and proximity measures, and iii) a guidance advice system. The multigranular linguistic methodology permits the unification of the different linguistic domains to facilitate the calculus of consensus degrees and proximity measures on the basis of experts' opinions. The consensus degrees assess the agreement amongst all the experts' opinions, while the proximity measures are used to find out how far the individual opinions are from the group opinion. The guidance advice system integrated in the consensus support system model acts as a feedback mechanism, and it is based on a set of advice rules to help the experts change their opinions and to find out which direction that change should follow in order to obtain the highest degree of consensus possible. There are two main advantages provided by this model of consensus support system. Firstly, its ability to cope with group decision-making problems with multigranular linguistic preference relations, and, secondly, the figure of the moderator, traditionally presents in the consensus reaching process, is replaced by the guidance advice system, and in such a way, the whole group decision-making process is automated.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9RI7MLEU\\Herrera-Viedma et al. - 2005 - A Consensus Support System Model for Group Decisio.pdf},
  langid = {english},
  number = {5}
}

@article{hevnerDesignScienceInformation2004,
  title = {Design {{Science}} in {{Information Systems Research}}},
  author = {Hevner, Alan and March, Salvatore T. and Park, Jinsoo and Ram, Sudha},
  date = {2004},
  journaltitle = {MIS Quaterly},
  volume = {28},
  pages = {75--105},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\WJHPP9MZ\\Hevner et al. - 2004 - Design Science in Information Systems Research.pdf},
  langid = {english},
  number = {1}
}

@article{hevnerRolesDigitalInnovation2019,
  title = {Roles of {{Digital Innovation}} in {{Design Science Research}}},
  author = {Hevner, Alan and vom Brocke, Jan and Maedche, Alexander},
  date = {2019-02},
  journaltitle = {Business \& Information Systems Engineering},
  shortjournal = {Bus Inf Syst Eng},
  volume = {61},
  pages = {3--8},
  issn = {2363-7005, 1867-0202},
  doi = {10.1007/s12599-018-0571-z},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\6AE5NV89\\Hevner et al. - 2019 - Roles of Digital Innovation in Design Science Rese.pdf},
  langid = {english},
  number = {1},
  options = {useprefix=true}
}

@article{hoangthuanConstructionDesignScience2019,
  title = {Construction of {{Design Science Research Questions}}},
  author = {Hoang Thuan, Nguyen and Drechsler, Andreas and Antunes, Pedro},
  date = {2019},
  journaltitle = {Communications of the Association for Information Systems},
  shortjournal = {CAIS},
  pages = {332--363},
  issn = {15293181},
  doi = {10.17705/1CAIS.04420},
  abstract = {Posing research questions is a fundamental step to guide and direct knowledge development in research. In design science research (DSR), research questions are important to define the scope and the modes of inquiry, characterize the artifacts, and communicate the contributions. Despite the importance of research questions, there are few guidelines on how to construct suitable DSR research questions. We fill this gap by exploring ways of constructing DSR research questions and analyzing the research questions in a sample of 104 DSR publications. The results show that about two thirds of the analyzed DSR publications actually use research questions to link their problem statements to research approaches and that most of the questions are aimed at problem-solving. Based on our analysis, we derive a typology of DSR question formulation to provide guidelines and patterns that help researchers formulate research questions during their DSR projects' duration.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\B7J5BEGT\\Hoang Thuan et al. - 2019 - Construction of Design Science Research Questions.pdf},
  langid = {english}
}

@article{hollingsheadRankOrderEffectGroup1996,
  title = {The {{Rank}}-{{Order Effect}} in {{Group Decision Making}}},
  author = {Hollingshead, Andrea B.},
  date = {1996-12},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  shortjournal = {Organizational Behavior and Human Decision Processes},
  volume = {68},
  pages = {181--193},
  issn = {07495978},
  doi = {10.1006/obhd.1996.0098},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GPRG7D8G\\Hollingshead - 1996 - The Rank-Order Effect in Group Decision Making.pdf},
  langid = {english},
  number = {3}
}

@online{IndustrySpecificProduct2020,
  title = {Industry Specific Product Configuration with {{CAS SmartCustomization}}},
  date = {2020-03-23},
  url = {https://www.cas-merlin.com/configuration-solutions.html},
  urldate = {2020-03-23},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9CJPCDKA\\configuration-solutions.html}
}

@incollection{jamesonRecommendationGroups2007,
  title = {Recommendation to {{Groups}}},
  booktitle = {The {{Adaptive Web}}: {{Methods}} and {{Strategies}} of {{Web Personalization}}},
  author = {Jameson, Anthony and Smyth, Barry},
  editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang},
  date = {2007},
  pages = {596--627},
  publisher = {{Springer}},
  location = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-540-72079-9_20},
  abstract = {Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user's preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\Q9DL2M5M\\Jameson_Smyth_2007_Recommendation to Groups.pdf},
  isbn = {978-3-540-72079-9},
  keywords = {Animated Character,Explicit Preference,Group Recommender,Individual Group Member,Recommender System},
  langid = {english},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@book{janis1982groupthink,
  title = {Groupthink: {{Psychological}} Studies of Policy Decisions and Fiascoes},
  author = {Janis, Irving Lester and Janis, Irving Lester},
  date = {1982},
  volume = {349},
  publisher = {{Houghton Mifflin Boston}}
}

@incollection{janisGroupthink1991,
  title = {Groupthink},
  booktitle = {A {{First Look}} at {{Communication Theory}}},
  author = {Janis, Irving},
  date = {1991},
  pages = {235--246},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3JN8DV3U\\griffin-groupthink-challenger.pdf}
}

@software{junEugeneeeoTinyrecord2020,
  title = {Eugene-Eeo/Tinyrecord},
  author = {Jun, Eeo},
  date = {2020-02-01T17:00:35Z},
  origdate = {2014-08-26T01:58:44Z},
  url = {https://github.com/eugene-eeo/tinyrecord},
  urldate = {2020-04-09},
  abstract = {transaction support for TinyDB. Contribute to eugene-eeo/tinyrecord development by creating an account on GitHub.}
}

@article{kerrBiasJudgmentComparing1996,
  title = {Bias in Judgment: {{Comparing}} Individuals and Groups.},
  author = {Kerr, Norbert L and MacCoun, Robert J and Kramer, Geoffrey P},
  date = {1996},
  journaltitle = {Psychological review},
  volume = {103},
  pages = {687},
  publisher = {{American Psychological Association}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\HQGR93PH\\Kerr et al_1996_Bias in judgment.pdf},
  keywords = {bias,group-decisions},
  number = {4}
}

@inproceedings{knijnenburgEachHisOwn2011,
  title = {Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems},
  shorttitle = {Each to His Own},
  booktitle = {Proceedings of the Fifth {{ACM}} Conference on {{Recommender}} Systems - {{RecSys}} '11},
  author = {Knijnenburg, Bart P. and Reijmer, Niels J.M. and Willemsen, Martijn C.},
  date = {2011},
  pages = {141--148},
  publisher = {{ACM Press}},
  location = {{Chicago, Illinois, USA}},
  doi = {10.1145/2043932.2043960},
  eventtitle = {The Fifth {{ACM}} Conference},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9MQQP8WF\\Knijnenburg et al_2011_Each to his own.pdf},
  isbn = {978-1-4503-0683-6},
  langid = {english}
}

@online{kuchelmeister13hannes11BachelorThesis,
  title = {13hannes11/Bachelor\_thesis\_m.Recommend},
  author = {Kuchelmeister, Hannes F.},
  journaltitle = {GitHub},
  url = {https://github.com/13hannes11/bachelor_thesis_m.recommend},
  urldate = {2020-05-12},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\8UURN4KL\\bachelor_thesis_m.html},
  langid = {english}
}

@article{likaFacingColdStart2014,
  title = {Facing the Cold Start Problem in Recommender Systems},
  author = {Lika, Blerina and Kolomvatsos, Kostas and Hadjiefthymiades, Stathes},
  date = {2014-03-01},
  journaltitle = {Expert Systems with Applications},
  shortjournal = {Expert Systems with Applications},
  volume = {41},
  pages = {2065--2073},
  issn = {0957-4174},
  doi = {10.1016/j.eswa.2013.09.005},
  abstract = {A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\MLFV7EWY\\Lika et al_2014_Facing the cold start problem in recommender systems.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\CW4M95HK\\S0957417413007240.html},
  issue = {4, Part 2},
  keywords = {Cold start problem,Recommender systems},
  langid = {english}
}

@inproceedings{liuCGSPAComprehensiveGroup2019,
  title = {{{CGSPA}}: {{Comprehensive Group Similarity Preference Aggregation Algorithm}} for {{Group Itinerary Recommendation System}}},
  shorttitle = {{{CGSPA}}},
  booktitle = {2019 {{IEEE}} 10th {{Annual Information Technology}}, {{Electronics}} and {{Mobile Communication Conference}} ({{IEMCON}})},
  author = {Liu, Xubo and Ma, Yuankai and Feng, Yutian and Tang, Huaixi and Dai, Zhitao},
  date = {2019-10},
  pages = {0750--0756},
  issn = {2644-3155},
  doi = {10.1109/IEMCON.2019.8936245},
  abstract = {One of the key factors to group multi-session recommendation system is about aggregating the preference of all users. To address the specific issues, we propose the Comprehensive Group Similarity Preference Aggregation (CGSPA) algorithm for Group Itinerary Recommendation System implemented in this paper. CGSPA comprehensively considers three aspects to aggregate group preference requirement, which are (1) the average maximum similarity between the recommendation plan and group preference; (2) the difference between the similarity of each session in the recommendation plan and the corresponding preference needs of the user group; (3) the historical similarity between the recommendation plan and group preference. We conduct extensive experiments to verify the preference aggregation performance and the robustness of CGSPA algorithm. We conclude that with different group scales, CGSPA's group average satisfaction score is higher compared to the traditional preference aggregation algorithms.},
  eventtitle = {2019 {{IEEE}} 10th {{Annual Information Technology}}, {{Electronics}} and {{Mobile Communication Conference}} ({{IEMCON}})},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5I8BJ3LW\\Liu et al_2019_CGSPA.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\B9MHB67G\\8936245.html},
  keywords = {CGSPA,Group Itinerary Recommendation,Preference Aggregation Algorithm,Recommendation System}
}

@article{loucaJointOptimizationProfit2019,
  title = {Joint {{Optimization}} of {{Profit}} and {{Relevance}} for {{Recommendation Systems}} in {{E}}-Commerce},
  author = {Louca, Raphael and Bhattacharya, Moumita and Hu, Diane and Hong, Liangjie},
  date = {2019},
  pages = {4},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\AMRZSDHT\\Louca et al. - 2019 - Joint Optimization of Profit and Relevance for Rec.pdf},
  langid = {english},
  note = {ZSCC: 0000001}
}

@article{malthouseMultistakeholderRecommenderSystems2019,
  title = {A {{Multistakeholder Recommender Systems Algorithm}} for {{Allocating Sponsored Recommendations}}},
  author = {Malthouse, Edward C and Vakeel, Khadija Ali and Hessary, Yasaman Kamyab and Burke, Robin and Fuduri\'c, Morana},
  date = {2019},
  pages = {8},
  abstract = {Retailing and social media platforms recommend two types of items to their users: sponsored items that generate ad revenue and nonsponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through some form of programmatic auction, and non-sponsored items to maximize user utility with a recommender system (RS). We develop a multiobjective binary integer programming model to allocate sponsored recommendations considering a dual objective of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method can be applied as a form of post processing to an existing RS, making it widely applicable. We apply the model to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach, which unifies programmatic advertising and RS, opens a new frontier for advertising and RS research and we therefore provide an extended discussion of future research topics.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\ZCMF3A2J\\Malthouse et al. - 2019 - A Multistakeholder Recommender Systems Algorithm f.pdf},
  langid = {english},
  note = {ZSCC: 0000001}
}

@book{martinCleanArchitectureCraftsman2017,
  title = {Clean {{Architecture}}: {{A Craftsman}}'s {{Guide}} to {{Software Structure}} and {{Design}}},
  shorttitle = {Clean {{Architecture}}},
  author = {Martin, Robert C.},
  date = {2017},
  edition = {1st},
  publisher = {{Prentice Hall Press}},
  location = {{USA}},
  abstract = {Practical Software Architecture Solutions from the Legendary Robert C. Martin (Uncle Bob) By applying universal rules of software architecture, you can dramatically improve developer productivity throughout the life of any software system. Now, building upon the success of his best-selling books Clean Code and The Clean Coder, legendary software craftsman Robert C. Martin (Uncle Bob) reveals those rules and helps you apply them. Martins Clean Architecture doesnt merely present options. Drawing on over a half-century of experience in software environments of every imaginable type, Martin tells you what choices to make and why they are critical to your success. As youve come to expect from Uncle Bob, this book is packed with direct, no-nonsense solutions for the real challenges youll facethe ones that will make or break your projects. Learn what software architects need to achieveand core disciplines and practices for achieving it Master essential software design principles for addressing function, component separation, and data management See how programming paradigms impose discipline by restricting what developers can do Understand whats critically important and whats merely a detail Implement optimal, high-level structures for web, database, thick-client, console, and embedded applications Define appropriate boundaries and layers, and organize components and services See why designs and architectures go wrong, and how to prevent (or fix) these failures Clean Architecture is essential reading for every current or aspiring software architect, systems analyst, system designer, and software managerand for every programmer who must execute someone elses designs. Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.},
  isbn = {978-0-13-449416-6},
  pagetotal = {432}
}

@incollection{Masthoff2015,
  title = {Group Recommender Systems: {{Aggregation}}, Satisfaction and Group Attributes},
  booktitle = {Recommender Systems Handbook},
  author = {Masthoff, Judith},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha},
  date = {2015},
  pages = {743--776},
  publisher = {{Springer US}},
  location = {{Boston, MA}},
  doi = {10.1007/978-1-4899-7637-6\%00822},
  abstract = {Thisgroup recommender systemsatisfactionchapter shows how a system can recommend to a group of users by aggregating information from individual user models and modeling the user's affective stateaffective state. It summarizes results from previous research in these areas. It explores how group attributes can be incorporated in aggregationaggregationstrategies. Additionally, it shows how group recommendationgroup recommender systemtechniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.},
  isbn = {978-1-4899-7637-6}
}

@online{MatplotlibDocumentation,
  title = {Matplotlib 3.2.1 Documentation},
  url = {https://matplotlib.org/},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\C75Q6QS8\\matplotlib.org.html}
}

@incollection{mazoRecommendationHeuristicsImproving2014,
  title = {Recommendation {{Heuristics}} for {{Improving Product Line Configuration Processes}}},
  booktitle = {Recommendation {{Systems}} in {{Software Engineering}}},
  author = {Mazo, Ra\'ul and Dumitrescu, Cosmin and Salinesi, Camille and Diaz, Daniel},
  editor = {Robillard, Martin P. and Maalej, Walid and Walker, Robert J. and Zimmermann, Thomas},
  date = {2014},
  pages = {511--537},
  publisher = {{Springer}},
  location = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-45135-5_19},
  abstract = {In mass customization industries, such as car manufacturing, configurators play an important role both to interact with customers and in engineering processes. This is particularly true when engineers rely on reuse of assets and product line engineering techniques. Theoretically, product line configuration should be guided by the product line model. However, in the industrial context, the configuration of products from product line models is complex and error-prone due to the large number of variables in the models. The configuration activity quickly becomes cumbersome due to the number of decisions needed to get a proper configuration, to the fact that they should be taken in predefined order, or the poor response time of configurators when decisions are not appropriate. This chapter presents a collection of recommendation heuristics to improve the interactivity of product line configuration so as to make it scalable to common engineering situations. We describe the principles, benefits, and the implementation of each heuristic using constraint programming. The application and usability of the heuristics is demonstrated using a case study from the car industry.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\IB6XF8FF\\Mazo et al_2014_Recommendation Heuristics for Improving Product Line Configuration Processes.pdf},
  isbn = {978-3-642-45135-5},
  keywords = {Configuration Process,Constraint Program,Product Line,Product Line Engineering,Variation Point},
  langid = {english}
}

@inproceedings{mcsherrySimilarityCompromise2003,
  title = {Similarity and {{Compromise}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {McSherry, David},
  editor = {Ashley, Kevin D. and Bridge, Derek G.},
  date = {2003},
  pages = {291--305},
  publisher = {{Springer}},
  location = {{Berlin, Heidelberg}},
  doi = {10.1007/3-540-45006-8_24},
  abstract = {A common cause of retrieval failure in case-based reasoning (CBR) approaches to product recommendation is that the retrieved cases, usually those that are most similar to the target query, are not sufficiently representative of compromises that the user may be prepared to make. We present a new approach to retrieval in which similarity and compromise play complementary roles, thereby increasing the likelihood that one of the retrieved cases will be acceptable to the user. We also show how the approach can be extended to address the requirements of domains in which the user is not just seeking a single item that closely matches her query, but would like to be informed of all items that are likely to be of interest.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\G43RIHRD\\McSherry_2003_Similarity and Compromise.pdf},
  isbn = {978-3-540-45006-1},
  keywords = {Average Success Rate,Case Library,Monitor Size,Query Refinement,Recommender System},
  langid = {english},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@article{mendoncaCollaborativeProductConfiguration2008,
  title = {Collaborative {{Product Configuration}}},
  author = {Mendon\c{c}a, Marc\'ilio and Cowan, Donald and Malyk, William and Oliveira, Toacy},
  date = {2008-01},
  journaltitle = {Journal of Software},
  shortjournal = {JSW},
  volume = {3},
  pages = {69--82},
  abstract = {In the Software Product Line approach, product configuration is a key activity in which stakeholders choose features for a product. This activity is critical in the sense that careless feature selections might lead to undesirable products. Even though product configuration is seen as a team activity in which divergent interests and views are merged into a single consistent product specification, current configuration technology is essentially single-userbased. This configuration approach can be error-prone and time-consuming as it usually requires numerous interactions between the product manager and the stakeholders to resolve decision conflicts. To tackle this problem we have proposed an approach called ``Collaborative Product Configuration'' (CPC). In this paper, we extend the CPC approach by providing efficient dependency analysis algorithms to support the validation of workflow-based descriptions called CPC plans. In addition, we add to previous work by providing a formal description of the approach's concepts, an augmented illustrated example, and a discussion covering several prototype tools now available.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\RWRMVBZ2\\Mendonça et al. - 2008 - Collaborative Product Configuration.pdf},
  langid = {english},
  number = {2}
}

@article{mendoncaDecisionmakingCoordinationEfficient2010,
  title = {Decision-Making Coordination and Efficient Reasoning Techniques for Feature-Based Configuration},
  author = {Mendonca, Marcilio and Cowan, Donald},
  date = {2010-05},
  journaltitle = {Science of Computer Programming},
  shortjournal = {Science of Computer Programming},
  volume = {75},
  pages = {311--332},
  issn = {01676423},
  doi = {10.1016/j.scico.2009.12.004},
  abstract = {Software Product Lines is a contemporary approach to software development that exploits the similarities and differences within a family of systems in a particular domain of interest in order to provide a common infrastructure for deriving members of this family in a timely fashion, with high-quality standards, and at lower costs.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\PKMHKVGL\\Mendonca und Cowan - 2010 - Decision-making coordination and efficient reasoni.pdf},
  langid = {english},
  number = {5}
}

@article{milchIndividualPreferenceConstruction2009,
  title = {From Individual Preference Construction to Group Decisions: {{Framing}} Effects and Group Processes},
  shorttitle = {From Individual Preference Construction to Group Decisions},
  author = {Milch, Kerry F. and Weber, Elke U. and Appelt, Kirstin C. and Handgraaf, Michel J.J. and Krantz, David H.},
  date = {2009-03},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  shortjournal = {Organizational Behavior and Human Decision Processes},
  volume = {108},
  pages = {242--255},
  issn = {07495978},
  doi = {10.1016/j.obhdp.2008.11.003},
  abstract = {Two choice tasks known to produce framing effects in individual decisions were used to test group sensitivity to framing, relative to that of individuals, and to examine the effect of prior, individual consideration of a decision on group choice. Written post-decision reasons and pre-decision group discussions were analyzed to investigate process explanations of choices made by preexisting, naturalistic groups. For a risky choice problem, a similar framing effect was observed for groups and individuals. For an intertemporal choice task where consumption was either delayed or accelerated, na\"ive groups (whose members had not preconsidered the decision) showed a framing effect, less discounting in the delay frame, opposite to that observed in individuals. Predecided groups showed a non-significant effect in the other, expected direction. In all cases, process measures better explained variability in choices across conditions than frame alone. Implications for group decision research and design considerations for committee decisions are addressed.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\V444AHG9\\Milch et al. - 2009 - From individual preference construction to group d.pdf},
  langid = {english},
  number = {2}
}

@article{murphyPrimacyRecencyEffects2006,
  title = {Primacy and {{Recency Effects}} on {{Clicking Behavior}}},
  author = {Murphy, Jamie and Hofacker, Charles and Mizerski, Richard},
  date = {2006-01},
  journaltitle = {Journal of Computer-Mediated Communication},
  shortjournal = {J Comp Mediated Comm},
  volume = {11},
  pages = {522--535},
  issn = {1083-6101, 1083-6101},
  doi = {10.1111/j.1083-6101.2006.00025.x},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\29P4CLMW\\Murphy et al. - 2006 - Primacy and Recency Effects on Clicking Behavior.pdf},
  langid = {english},
  number = {2}
}

@inproceedings{ninausINTELLIREQIntelligentTechniques2014,
  title = {{{INTELLIREQ}}: {{Intelligent Techniques}} for {{Software Requirements Engineering}}},
  booktitle = {Prestigious {{Applications}} of {{Intelligent Systems}}},
  author = {Ninaus, Gerald and Felfernig, Alexander and Stettinger, Martin and Reiterer, Stefan and Leitner, Gerhard and Weninger, Leopold and Schanil, Walter},
  date = {2014},
  pages = {1161--1166},
  location = {{Montpellier, France}},
  abstract = {Requirements Engineering is considered as one of the most critical phases of a software development project. Low-quality requirements are a major reason for the failure of a project. Consequently, techniques are needed that help to improve the support of stakeholders in the development of requirements models as well as in the process of deciding about the corresponding release plans. In this paper we introduce the INTELLIREQ Requirements Engineering environment. This environment is based on different recommendation approaches that support stakeholders in requirements-related activities such as definition, quality assurance, reuse, and release planning. We provide an overview of recommendation approaches integrated in INTELLIREQ and report results of empirical studies that show in which way recommenders can improve the quality of Requirements Engineering processes.},
  eventtitle = {European {{Conference}} on {{Artificial Intelligence}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\YKJ2AIB9\\Ninaus et al. - INTELLIREQ Intelligent Techniques for Software Re.pdf},
  langid = {english}
}

@incollection{ningComprehensiveSurveyNeighborhoodBased2015,
  title = {A {{Comprehensive Survey}} of {{Neighborhood}}-{{Based Recommendation Methods}}},
  booktitle = {Recommender {{Systems Handbook}}},
  author = {Ning, Xia and Desrosiers, Christian and Karypis, George},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha},
  date = {2015},
  pages = {37--76},
  publisher = {{Springer US}},
  location = {{Boston, MA}},
  doi = {10.1007/978-1-4899-7637-6_2},
  abstract = {Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and some solutions to overcome these problems are presented.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\D6HDEGBT\\Ning et al_2015_A Comprehensive Survey of Neighborhood-Based Recommendation Methods.pdf},
  isbn = {978-1-4899-7637-6},
  keywords = {Diffusion Kernel,Recommendation Approach,Recommender System,Side Information,Similarity Weight},
  langid = {english}
}

@online{NumPy,
  title = {{{NumPy}}},
  url = {https://numpy.org/},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3QGT59ZB\\numpy.org.html}
}

@inproceedings{offermannOutlineDesignScience2009,
  title = {Outline of a Design Science Research Process},
  booktitle = {Proceedings of the 4th {{International Conference}} on {{Design Science Research}} in {{Information Systems}} and {{Technology}} - {{DESRIST}} '09},
  author = {Offermann, Philipp and Levina, Olga and Sch\"onherr, Marten and Bub, Udo},
  date = {2009},
  pages = {1},
  publisher = {{ACM Press}},
  location = {{Philadelphia, Pennsylvania}},
  abstract = {Discussions about the body of knowledge of information systems, including the research domain, relevant perspectives and methods have been going on for a long time. Many researchers vote for a combination of research perspectives and their respective research methodologies; rigour and relevance as requirements in design science are generally accepted. What has been lacking is a formalisation of a detailed research process for design science that takes into account all requirements. We have developed such a research process, building on top of existing processes and findings from design research. The process combines qualitative and quantitative research and references well-known research methods. Publication possibilities and self-contained work packages are recommended. Case studies using the process are presented and discussed.},
  eventtitle = {The 4th {{International Conference}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\8BE6HMR6\\Offermann et al. - 2009 - Outline of a design science research process.pdf},
  isbn = {978-1-60558-408-9},
  langid = {english}
}

@online{PandasPythonData,
  title = {Pandas - {{Python Data Analysis Library}}},
  url = {https://pandas.pydata.org/},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FD9I7UUJ\\pandas.pydata.org.html}
}

@article{peffersDesignScienceResearch2007,
  title = {A {{Design Science Research Methodology}} for {{Information Systems Research}}},
  author = {Peffers, Ken and Tuunanen, Tuure and Rothenberger, Marcus A. and Chatterjee, Samir},
  date = {2007-12},
  journaltitle = {Journal of Management Information Systems},
  shortjournal = {Journal of Management Information Systems},
  volume = {24},
  pages = {45--77},
  issn = {0742-1222, 1557-928X},
  abstract = {The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems (IS). DS is of importance in a discipline oriented to the creation of successful artifacts. Several researchers have pioneered DS research in IS, yet over the past 15 years, little DS research has been done within the discipline. The lack of a methodology to serve as a commonly accepted framework for DS research and of a template for its presentation may have contributed to its slow adoption. The design science research methodology (DSRM) presented here incorporates principles, practices, and procedures required to carry out such research and meets three objectives: it is consistent with prior literature, it provides a nominal process model for doing DS research, and it provides a mental model for presenting and evaluating DS research in IS. The DS process includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. We demonstrate and evaluate the methodology by presenting four case studies in terms of the DSRM, including cases that present the design of a database to support health assessment methods, a software reuse measure, an Internet video telephony application, and an IS planning method. The designed methodology effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\7A9P9P9J\\Peffers et al. - 2007 - A Design Science Research Methodology for Informat.pdf},
  langid = {english},
  number = {3}
}

@article{peffersDesignScienceResearch2007a,
  title = {A {{Design Science Research Methodology}} for {{Information Systems Research}}},
  author = {Peffers, Ken and Tuunanen, Tuure and Rothenberger, Marcus A. and Chatterjee, Samir},
  date = {2007-12},
  journaltitle = {Journal of Management Information Systems},
  shortjournal = {Journal of Management Information Systems},
  volume = {24},
  pages = {45--77},
  issn = {0742-1222, 1557-928X},
  doi = {10.2753/MIS0742-1222240302},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GCVWBMVC\\Peffers et al. - 2007 - A Design Science Research Methodology for Informat.pdf},
  langid = {english},
  number = {3}
}

@article{peraGroupRecommenderMovies2013,
  title = {A Group Recommender for Movies Based on Content Similarity and Popularity},
  author = {Pera, Maria S. and Ng, Yiu-Kai},
  date = {2013-05-01},
  journaltitle = {Information Processing \& Management},
  shortjournal = {Information Processing \& Management},
  volume = {49},
  pages = {673--687},
  issn = {0306-4573},
  doi = {10.1016/j.ipm.2012.07.007},
  abstract = {People are gregarious by nature, which explains why group activities, from colleagues sharing a meal to friends attending a book club event together, are the social norm. Online group recommenders identify items of interest, such as restaurants, movies, and books, that satisfy the collective needs of a group (rather than the interests of individual group members). With a number of new movies being released every week, online recommenders play a significant role in suggesting movies for family members or groups of friends/people to watch, either at home or at movie theaters. Making group recommendations relevant to the joint interests of a group, however, is not a trivial task due to the diversity in preferences among group members. To address this issue, we introduce GroupReM which makes movie recommendations appealing (to a certain degree) to members of a group by (i) employing a merging strategy to explore individual group members' interests in movies and create a profile that reflects the preferences of the group on movies, (ii) using word-correlation factors to find movies similar in content, and (iii) considering the popularity of movies at a movie website. Unlike existing group recommenders based on collaborative filtering (CF) which consider ratings of movies to perform the recommendation task, GroupReM primarily employs (personal) tags for capturing the contents of movies considered for recommendation and group members' interests. The design of GroupReM, which is simple and domain-independent, can easily be extended to make group recommendations on items other than movies. Empirical studies conducted using more than 3000 groups of different users in the MovieLens dataset, which are various in terms of numbers and preferences in movies, show that GroupReM is highly effective and efficient in recommending movies appealing to a group. Experimental results also verify that GroupReM outperforms popular CF-based recommenders in making group recommendations.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\KXUX8URG\\Pera_Ng_2013_A group recommender for movies based on content similarity and popularity.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\BWWR8DFF\\S0306457312001045.html},
  keywords = {Content-similarity,Group recommender,Movie,Popularity},
  langid = {english},
  number = {3},
  series = {Personalization and {{Recommendation}} in {{Information Access}}}
}

@article{pereiraFeatureBasedPersonalizedRecommender2016,
  title = {A {{Feature}}-{{Based Personalized Recommender System}} for {{Product}}-{{Line Configuration}}},
  author = {Pereira, Juliana Alves and Matuszyk, Pawel and Krieter, Sebastian and Spiliopoulou, Myra and Saake, Gunter},
  date = {2016},
  pages = {12},
  abstract = {Today's competitive marketplace requires the industry to understand unique and particular needs of their customers. Product line practices enable companies to create individual products for every customer by providing an interdependent set of features. Users configure personalized products by consecutively selecting desired features based on their individual needs. However, as most features are interdependent, users must understand the impact of their gradual selections in order to make valid decisions. Thus, especially when dealing with large feature models, specialized assistance is needed to guide the users in configuring their product. Recently, recommender systems have proved to be an appropriate mean to assist users in finding information and making decisions. In this paper, we propose an advanced feature recommender system that provides personalized recommendations to users. In detail, we offer four main contributions: (i) We provide a recommender system that suggests relevant features to ease the decision-making process. (ii) Based on this system, we provide visual support to users that guides them through the decision-making process and allows them to focus on valid and relevant parts of the configuration space. (iii) We provide an interactive open-source configurator tool encompassing all those features. (iv) In order to demonstrate the performance of our approach, we compare three different recommender algorithms in two real case studies derived from business experience.},
  langid = {english}
}

@inproceedings{pereiraFeaturebasedPersonalizedRecommender2016,
  title = {A Feature-Based Personalized Recommender System for Product-Line Configuration},
  booktitle = {Proceedings of the 2016 {{ACM SIGPLAN}} International Conference on Generative Programming: {{Concepts}} and Experiences},
  author = {Pereira, Juliana Alves and Matuszyk, Pawel and Krieter, Sebastian and Spiliopoulou, Myra and Saake, Gunter},
  date = {2016},
  pages = {120--131},
  publisher = {{Association for Computing Machinery}},
  location = {{Amsterdam, Netherlands}},
  doi = {10.1145/2993236.2993249},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\2RZL7FT7\\Pereira et al. - A Feature-Based Personalized Recommender System fo.pdf},
  isbn = {978-1-4503-4446-3},
  keywords = {Personalized Recommendations,Product-Line Configuration,Recommenders,Software Product Lines},
  numpages = {12},
  series = {{{GPCE}} 2016}
}

@article{pereiraPersonalizedRecommenderSystems2018,
  title = {Personalized Recommender Systems for Product-Line Configuration Processes},
  author = {Pereira, Juliana Alves and Matuszyk, Pawel and Krieter, Sebastian and Spiliopoulou, Myra and Saake, Gunter},
  date = {2018-12-01},
  journaltitle = {Computer Languages, Systems \& Structures},
  shortjournal = {Computer Languages, Systems \& Structures},
  volume = {54},
  pages = {451--471},
  issn = {1477-8424},
  doi = {10.1016/j.cl.2018.01.003},
  abstract = {Product lines are designed to support the reuse of features across multiple products. Features are product functional requirements that are important to stakeholders. In this context, feature models are used to establish a reuse platform and allow the configuration of multiple products through the interactive selection of a valid combination of features. Although there are many specialized configurator tools that aim to provide configuration support, they only assure that all dependencies from selected features are automatically satisfied. However, no support is provided to help decision makers focus on likely relevant configuration options. Consequently, since decision makers are often unsure about their needs, the configuration of large feature models becomes challenging. To improve the efficiency and quality of the product configuration process, we propose a new approach that provides users with a limited set of permitted, necessary and relevant choices. To this end, we adapt six state-of-the-art recommender algorithms to the product line configuration context. We empirically demonstrate the usability of the implemented algorithms in different domain scenarios, based on two real-world datasets of configurations. The results of our evaluation show that recommender algorithms, such as CF-shrinkage, CF-significance weighting, and BRISMF, when applied in the context of product-line configuration can efficiently support decision makers in a most efficient selection of features.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\YM7DR8TY\\S147784241730043X.html},
  keywords = {Feature model,Personalized recommendations,Product lines,Product-line configuration,Recommender systems},
  langid = {english}
}

@article{pereiraPersonalizedRecommenderSystems2018a,
  title = {Personalized Recommender Systems for Product-Line Configuration Processes},
  author = {Pereira, Juliana Alves and Matuszyk, Pawel and Krieter, Sebastian and Spiliopoulou, Myra and Saake, Gunter},
  date = {2018-12},
  journaltitle = {Computer Languages, Systems \& Structures},
  shortjournal = {Computer Languages, Systems \& Structures},
  volume = {54},
  pages = {451--471},
  issn = {14778424},
  doi = {10.1016/j.cl.2018.01.003},
  abstract = {Product lines are designed to support the reuse of features across multiple products. Features are product functional requirements that are important to stakeholders. In this context, feature models are used to establish a reuse platform and allow the configuration of multiple products through the interactive selection of a valid combination of features. Although there are many specialized configurator tools that aim to provide configuration support, they only assure that all dependencies from selected features are automatically satisfied. However, no support is provided to help decision makers focus on likely relevant configuration options. Consequently, since decision makers are often unsure about their needs, the configuration of large feature models becomes challenging. To improve the efficiency and quality of the product configuration process, we propose a new approach that provides users with a limited set of permitted, necessary and relevant choices. To this end, we adapt six state-of-the-art recommender algorithms to the product line configuration context. We empirically demonstrate the usability of the implemented algorithms in different domain scenarios, based on two real-world datasets of configurations. The results of our evaluation show that recommender algorithms, such as CF-shrinkage, CF-significance weighting, and BRISMF, when applied in the context of product-line configuration can efficiently support decision makers in a most efficient selection of features.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3LYWEVPZ\\Pereira et al. - 2018 - Personalized recommender systems for product-line .pdf},
  langid = {english}
}

@inproceedings{piliponyte2013sequential,
  title = {Sequential Music Recommendations for Groups by Balancing User Satisfaction.},
  booktitle = {{{UMAP}} Workshops},
  author = {Piliponyte, Auste and Ricci, Francesco and Koschwitz, Julian},
  date = {2013},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\Y9EFUITE\\Piliponyte et al. - Sequential Music Recommendations for Groups by Bal.pdf}
}

@unpublished{pydataExamplePredictiveAnalytics16:42:00UTC,
  title = {An {{Example}} of {{Predictive Analytics}}: {{Building}} a {{Recommendation Engine}} \ldots{}},
  shorttitle = {An {{Example}} of {{Predictive Analytics}}},
  author = {PyData},
  year = {16:42:00 UTC},
  url = {https://www.slideshare.net/PyData/an-example-of-predictive-analytics-building-a-recommendation-engine-using-pythonanusua-trivedi},
  urldate = {2020-02-19},
  abstract = {PyData Seattle 2015},
  type = {Data \& Analytics}
}

@online{PytestDocumentation,
  title = {Pytest Documentation},
  url = {https://docs.pytest.org/en/latest/},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\EHSWVWHW\\latest.html}
}

@online{PythonOrg,
  title = {Python.Org},
  url = {https://www.python.org/},
  urldate = {2020-04-09},
  abstract = {The official home of the Python Programming Language},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\ZKIC9FPT\\www.python.org.html},
  langid = {english}
}

@article{qiuInfluenceGroupConfiguration2015,
  title = {Influence of Group Configuration on Online Discourse Reading},
  author = {Qiu, Mingzhu and McDougall, Douglas},
  date = {2015-09},
  journaltitle = {Computers \& Education},
  shortjournal = {Computers \& Education},
  volume = {87},
  pages = {151--165},
  issn = {03601315},
  doi = {10.1016/j.compedu.2015.04.006},
  abstract = {Online discourse reading plays a very important role in collaborative discussions. However, not many studies have examined the influence of group configuration on online discourse note reading. The current study examined note reading workloads and participants' perceptions of the three group configurations (large whole class, small whole class, large with subgroups) in online graduate-level courses from one institute. In this mixed-methods study, we analyzed tracking logs from 25 graduate-level online courses (25 instructors and 341 students) and interviews from 10 instructors and 12 graduate students with diverse backgrounds. Findings suggest that all three configurations had their own advantages and disadvantages in fostering online discourse reading. However, our analysis suggests that the advantages of subgroup discussions in supporting note reading outweigh those of the Small and Large configurations. The overload effects in information reading due to large class sizes can be minimized by dividing students into small groups for discussion purposes. Group configuration into proper-size groups may reduce students' reading loads. Interviewees felt that the waving of small groups into large classes benefited their collaborative discussions. We conclude this paper with a list of pedagogical recommendations and new software features that may help group configuration and enhance learning in online courses. This study may have implications for both practitioners and researchers to seek optimal group configurations to achieve more fruitful online discussions through note reading.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\79AJNSBV\\Qiu und McDougall - 2015 - Influence of group configuration on online discour.pdf},
  langid = {english}
}

@thesis{raabKollaborativeProduktkonfigurationEchtzeit2019,
  title = {Kollaborative Produktkonfiguration in Echtzeit},
  author = {Raab, Fabian},
  date = {2019-09-30},
  institution = {{Hochschule Karlsruhe}},
  location = {{Karlsruhe}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\PPAL2KE6\\Raab - 2019 - Kollaborative Produktkonfiguration in Echtzeit.pdf},
  langid = {german},
  pagetotal = {83},
  type = {Master's thesis}
}

@inproceedings{rennebergPipelinedFilterCombination2003,
  title = {Pipelined {{Filter Combination}} in {{Product Personalization}}},
  booktitle = {Proc. 10th {{Int}}'l. {{Conf}}. on {{Human}}-{{Computer Interaction}}},
  author = {Renneberg, Volker and Borghoff, Uwe M},
  date = {2003},
  pages = {602--606},
  location = {{Crete, Greece}},
  eventtitle = {{{HCI}} 2003},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\DR8RWVGQ\\Renneberg and Borghoff - Pipelined Filter Combination in Product Personaliz.pdf},
  langid = {english}
}

@collection{ricciRecommenderSystemsHandbook2015,
  title = {Recommender Systems Handbook},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha},
  date = {2015},
  edition = {Second edition},
  publisher = {{Springer}},
  location = {{New York Heidelberg Dordrecht London}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\26HADE8N\\Ricci et al. - 2015 - Recommender systems handbook.pdf},
  isbn = {978-1-4899-7636-9 978-1-4899-7637-6},
  langid = {english},
  note = {ZSCC: NoCitationData[s0] 
OCLC: 935904837},
  pagetotal = {1003}
}

@article{richthammerSituationAwarenessRecommender2018,
  title = {Situation Awareness for Recommender Systems},
  author = {Richthammer, Christian and Pernul, G\"unther},
  date = {2018-10-24},
  journaltitle = {Electronic Commerce Research},
  shortjournal = {Electron Commer Res},
  issn = {1389-5753, 1572-9362},
  doi = {10.1007/s10660-018-9321-z},
  abstract = {One major shortcoming of traditional recommender systems is their inability to adjust to users' short-term preferences resulting from varying situation-specific factors. To address this, we propose the notion of situationaware recommender systems, which are supposed to autonomously determine the users' current situation based on a multitude of contextual side information and generate truly personalized recommendations. In particular, we develop a situation awareness model for recommender systems, include it in a situationaware recommendation process, and derive generic design steps for the design of situation-aware recommender systems. The feasibility of these concepts is demonstrated by directly employing them for the development and implementation of a music recommender system for everyday situations. Moreover, their meaningfulness is shown by means of an empirical user study. The outcomes of the evaluation indicate a significant increase in user satisfaction compared to traditional (i.e. non-situation-aware) recommendations.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\LNRJQH7H\\Richthammer and Pernul - 2018 - Situation awareness for recommender systems.pdf},
  langid = {english}
}

@thesis{rubinshteynEntwicklungHybridenRecommender2018,
  title = {Entwicklung eines hybriden Recommender Systems f\"ur die Produktkonfiguration},
  author = {Rubinshteyn, Alexander},
  date = {2018-11-09},
  institution = {{Karlsruhe Institute of Technology}},
  location = {{Karlsruhe}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\JXXN9BU8\\Rubinshteyn - Entwicklung eines hybriden Recommender Systems für.pdf},
  langid = {german},
  pagetotal = {98},
  type = {Master's thesis}
}

@unpublished{s.dianahuRecSys2015Tutorial23:21:24UTC,
  title = {{{RecSys}} 2015 {{Tutorial}}  \textendash{} {{Scalable Recommender Systems}}: {{Where Machine}} \ldots{}},
  shorttitle = {{{RecSys}} 2015 {{Tutorial}}  \textendash{} {{Scalable Recommender Systems}}},
  author = {S. Diana Hu},
  year = {23:21:24 UTC},
  url = {https://www.slideshare.net/SDianaHu/recsys-2015-tutorial-scalable-recommender-systems-where-machine-learning-meets-search},
  urldate = {2020-02-19},
  abstract = {Search engines have focused on solving the document retrieval problem, so},
  type = {Technology}
}

@article{sabinProductConfigurationFrameworksa1998,
  title = {Product Configuration Frameworks-a Survey},
  author = {Sabin, D. and Weigel, R.},
  date = {1998-07},
  journaltitle = {IEEE Intelligent Systems},
  shortjournal = {IEEE Intell. Syst.},
  volume = {13},
  pages = {42--49},
  issn = {1094-7167},
  doi = {10.1109/5254.708432},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\BG9A29JD\\Sabin und Weigel - 1998 - Product configuration frameworks-a survey.pdf},
  langid = {english},
  number = {4}
}

@inproceedings{samerGroupDecisionSupport2018,
  title = {Group {{Decision Support}} for {{Requirements Management Processes}}},
  author = {Samer, R and Atas, M and Felfernig, A and Stettinger, M and Falkner, A and Schenner, G},
  date = {2018},
  pages = {19--24},
  abstract = {Requests for proposal (RFP) trigger company-internal requirements management (RM) processes in order to assure that offers comply with a given set of customer requirements. As traditional RM approaches require a deep involvement of the requirements managers of a RM project especially when it comes to assigning suitable stakeholders to requirements, the quality of the decisions and the time effort for making correct decisions mainly depends on these experts. In this paper, we present a novel stakeholder assignment approach that reduces the overall involvement of these experts and also limits the uncertainty of overseeing suitable stakeholders at the same time. The assignment of responsible stakeholders is represented as a group decision task expressed in the form of a basic configuration problem. The outcome of such a task is a configuration which is represented in terms of an assignment of responsible stakeholders to corresponding requirements.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\7CZIDRDJ\\Samer et al. - Group Decision Support for Requirements Management.pdf},
  langid = {english}
}

@article{scholzConfigurationbasedRecommenderSystem2017,
  title = {A Configuration-Based Recommender System for Supporting e-Commerce Decisions},
  author = {Scholz, Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander},
  date = {2017-05},
  journaltitle = {European Journal of Operational Research},
  shortjournal = {European Journal of Operational Research},
  volume = {259},
  pages = {205--215},
  issn = {03772217},
  doi = {10.1016/j.ejor.2016.09.057},
  abstract = {Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\MUE33V9K\\Scholz et al. - 2017 - A configuration-based recommender system for suppo.pdf},
  langid = {english},
  number = {1}
}

@article{scholzEffectsDecisionSpace2017,
  title = {Effects of Decision Space Information on {{MAUT}}-Based Systems That Support Purchase Decision Processes},
  author = {Scholz, Michael and Franz, Markus and Hinz, Oliver},
  date = {2017-05},
  journaltitle = {Decision Support Systems},
  shortjournal = {Decision Support Systems},
  volume = {97},
  pages = {43--57},
  issn = {01679236},
  doi = {10.1016/j.dss.2017.03.004},
  abstract = {This paper shows that decision makers often have a misconception of the decision space. The decision space is constituted by the relations among the attributes describing the alternatives available in a decision situation. The paper demonstrates that these misconceptions negatively affect the usage and perceptions of MAUT-based decision support systems. To overcome these negative effects, this paper proposes to use a visualization method based on singular value decomposition to give decision makers insights into the attribute relations. In a laboratory experiment in cooperation with Germany's largest Internet real estate website, this paper moreover evaluates the proposed solution and shows that our solution improves decision makers' usage and perceptions of MAUT-based decision support systems. We further show that information about the decision space ultimately affects variables relevant for the economic success of decision support system providers such as reuse intention and the probability to act as a promoter for the systems.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\939QYBKB\\Scholz et al. - 2017 - Effects of decision space information on MAUT-base.pdf},
  langid = {english}
}

@article{schulz-hardtProductiveConflictGroup2002,
  title = {Productive Conflict in Group Decision Making: Genuine and Contrived Dissent as Strategies to Counteract Biased Information Seekingq},
  author = {Schulz-Hardt, Stefan and Jochims, Marc and Frey, Dieter},
  date = {2002},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  pages = {24},
  abstract = {Decision-making groups in organizations are often expected to function as a ``think tank'' and to perform ``reality testing'' to detect the best alternative. A biased search for information supporting the group's favored alternative impairs a group's ability to fulfill these requirements. In a two-factorial experiment with 201 employees and managers from various economic and public organizations, genuine and contrived dissent were investigated as counterstrategies to biased information seeking. Genuine dissent was manipulated by forming three-person groups whose members either all favored the same alternative individually (homogeneous groups) or consisted of a minority and a majority faction with regard to their favored alternative (heterogeneous groups). Contrived dissent was varied by the use or nonuse of the ``devil's advocacy'' technique. The results demonstrate that heterogeneity was more effective in preventing a confirmatory information-seeking bias than devil's advocacy was. Confidence was identified as an important mediator. Implications for the design of interventions aimed at facilitating reality testing in group decision making are discussed. \'O 2002 Elsevier Science (USA). All rights reserved.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\6C92UPS6\\Schulz-Hardt et al. - 2002 - Productive conﬂict in group decision making genui.pdf},
  langid = {english}
}

@inproceedings{shafieeCostBenefitAnalysis2018,
  title = {Cost {{Benefit Analysis}} in {{Product Configuration Systems}}},
  booktitle = {Proceedings of the 20th {{Configuration Workshop}}},
  author = {Shafiee, Sara and Felfernig, Alexander and Hvam, Lars and Piroozfar, Poorang and Forza, Cipriano},
  date = {2018},
  volume = {2220},
  pages = {37--40},
  eventtitle = {{{ConfWS}}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3HAEAKPS\\Shafiee et al. - Cost Benefit Analysis in Product Configuration Sys.pdf},
  langid = {english}
}

@article{shishehchiOntologicalApproachKnowledge2012,
  title = {Ontological {{Approach}} in {{Knowledge Based Recommender System}} to {{Develop}} the {{Quality}} of {{E}}-Learning {{System}}},
  author = {Shishehchi, Saman and Banihashem, Seyed Yashar and Zin, Nor Azan Mat and Noah, Shahrul Azman Mohd},
  date = {2012},
  pages = {9},
  abstract = {The rapid growth of Internet technology and the explosion of educational resources, show the increasing importance of e-learning systems. Despite the importance of these systems, they suffer from the enormous learning materials. In recent years, recommender systems appeared to improve the quality of learning. Such systems were used in learning systems to provide the facilities during the learning process and help learners with a more accurate learning. Different recommendation techniques such as collaborative filtering, content based and the hybrid filtering were employed for e-learning domain. In addition to the importance of learner's needs in the learning process, also the training method for recommended learning materials should be important in this learning process. This paper aims to develop the knowledge based personalized e-learning recommendation system based on ontology. Furthermore, this study discusses about appropriate recommendation technique based on learning system characteristics. The first significant property of this study is the common ontology for learner and learner materials. The second property is referring to the developed pedagogy pattern for this recommendation. The learning materials filter according to the prerequisites of the learner request and learner's knowledge. Learner can ask any activities such as example or description, by using graphical user interface.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\AAEZDYL7\\Shishehchi et al. - 2012 - Ontological Approach in Knowledge Based Recommende.pdf},
  langid = {english}
}

@article{shokeenStudyFeaturesSocial2019,
  title = {A Study on Features of Social Recommender Systems},
  author = {Shokeen, Jyoti and Rana, Chhavi},
  date = {2019},
  journaltitle = {Artificial Intelligence Review},
  doi = {10.1007/s10462-019-09684-w},
  abstract = {Recommender system is an emerging field of research with the advent of World Wide Web and E-commerce. Recently, an increasing usage of social networking websites plausibly has a great impact on diverse facets of our lives in different ways. Initially, researchers used to consider recommender system and social networks as independent topics. With the passage of time, they realized the importance of merging the two to produce enhanced recommendations. The integration of recommender system with social networks produces a new system termed as social recommender system. In this study, we initially describe the concept of recommender system and social recommender system and then investigates different features of social networks that play a major role in generating effective recommendations. Each feature plays an essential role in giving good recommendations and resolving the issues of traditional recommender systems. Lastly, this paper also discusses future work in this area that can aid in enriching the quality of social recommender systems.}
}

@article{sniezekGroupsUncertaintyExamination1992,
  title = {Groups under Uncertainty: {{An}} Examination of Confidence in Group Decision Making},
  shorttitle = {Groups under Uncertainty},
  author = {Sniezek, Janet A},
  date = {1992-06},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  shortjournal = {Organizational Behavior and Human Decision Processes},
  volume = {52},
  pages = {124--155},
  issn = {07495978},
  doi = {10.1016/0749-5978(92)90048-C},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FB7B7PIK\\Sniezek - 1992 - Groups under uncertainty An examination of confid.pdf},
  keywords = {confidence in decions,group-decisions},
  langid = {english},
  number = {1}
}

@inproceedings{stegmannGeneratingPersonalizedRecommendations2003,
  title = {Generating {{Personalized Recommendations}} in a {{Model}}-{{Based Product Configurator System}}},
  author = {Stegmann, Rosmary and Koch, Michael and Lacher, Martin and Leckner, Thomas and Renneberg, Volker},
  date = {2003},
  pages = {6},
  abstract = {Web-based product configurator tools become increasingly important as a means for customerdriven configuration of highly configurable products today and mass customization in the future. However, with more and more possibilities to select from, the configuration process becomes too complex and tedious for the customer. In this paper, we give an overview of our ideas and concepts for supporting customers by providing personalized recommendations in different stages of the configuration process. We briefly sketch our concept for an architecture of a model-based configurator system and discuss the filtering mechanisms we plan to employ for generating recommendations.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\2RXD9ZAQ\\Stegmann et al. - Generating Personalized Recommendations in a Model.pdf},
  langid = {english}
}

@inproceedings{stettingerCounteractingAnchoringEffects2015,
  title = {Counteracting {{Anchoring Effects}} in {{Group Decision Making}}},
  booktitle = {User {{Modeling}}, {{Adaptation}} and {{Personalization}}},
  author = {Stettinger, Martin and Felfernig, Alexander and Leitner, Gerhard and Reiterer, Stefan},
  editor = {Ricci, Francesco and Bontcheva, Kalina and Conlan, Owen and Lawless, S\'eamus},
  date = {2015},
  pages = {118--130},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-20267-9_10},
  abstract = {Similar to single user decisions, group decisions can be affected by decision biases. In this paper we analyze anchoring effects as a specific type of decision bias in the context of group decision scenarios. On the basis of the results of a user study in the domain of software requirements prioritization we discuss results regarding the optimal time when preference information of other users should be disclosed to the current user. Furthermore, we show that explanations can increase the satisfaction of group members with various aspects of a group decision process (e.g., satisfaction with the decision and decision support quality).},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\WEJED597\\Stettinger et al_2015_Counteracting Anchoring Effects in Group Decision Making.pdf},
  isbn = {978-3-319-20267-9},
  keywords = {Anchoring effects,Decision biases,Group decision making,Recommender systems},
  langid = {english},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@misc{StudienUndPrufungsordnung2015,
  title = {Studien- Und {{Pr\"ufungsordnung}} Des {{Karlsruher Instituts}} F\"ur {{Technologie}} ({{KIT}}) F\"ur Den {{Bachelorstudiengang Informatik}}},
  date = {2015-09-29},
  url = {https://www.informatik.kit.edu/downloads/info%20bsc%20spo%202015.pdf},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\8TDXS8ES\\2015 - Studien- und Prüfungsordnung des Karlsruher Instit.pdf}
}

@article{suSurveyCollaborativeFiltering2009,
  title = {A {{Survey}} of {{Collaborative Filtering Techniques}}},
  author = {Su, Xiaoyuan and Khoshgoftaar, Taghi M.},
  date = {2009},
  journaltitle = {Advances in Artificial Intelligence},
  shortjournal = {Advances in Artificial Intelligence},
  volume = {2009},
  pages = {1--19},
  issn = {1687-7470, 1687-7489},
  doi = {10.1155/2009/421425},
  abstract = {As one of the most successful approaches to building recommender systems, collaborative filtering (
              CF
              ) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\LD2VDXXZ\\Su and Khoshgoftaar - 2009 - A Survey of Collaborative Filtering Techniques.pdf},
  langid = {english}
}

@online{TableComparisonRecommender,
  title = {Table 1 : {{The}} Comparison of Recommender Approaches Based on The...},
  shorttitle = {Table 1},
  journaltitle = {ResearchGate},
  url = {https://www.researchgate.net/figure/The-comparison-of-recommender-approaches-based-on-the-knowledge-impression_tbl1_51934748},
  urldate = {2020-02-19},
  abstract = {Download Table | The comparison of recommender approaches based on the knowledge impression from publication: Discovering The Impact Of Knowledge In Recommender Systems: A Comparative Study | Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and... | Recommender Systems, Information Science and Information Retrieval | ResearchGate, the professional network for scientists.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\QHYYFJVN\\The-comparison-of-recommender-approaches-based-on-the-knowledge-impression_tbl1_51934748.html},
  langid = {english}
}

@inproceedings{thumProductConfigurationWild2018,
  title = {Product {{Configuration}} in the {{Wild}}: {{Strategies}} for {{Conflicting Decisions}} in {{Web Configurators}}},
  booktitle = {{{ConfWS}}},
  author = {Th\"um, Thomas and Krieter, Sebastian and Schaefer, Ina},
  date = {2018},
  pages = {1--8},
  abstract = {Customization is omnipresent in our everyday live. There are web configurators to customize cars, trucks, bikes, computers, clothes, furniture, and food. At first glance, customization using configurators appears trivial; we simply select the configuration options that we want. However, in practice, options are usually dependent on each other. Reasons for dependencies are manifold and are typically specific for the particular domain. Dependencies can be simple, such as one option requiring or excluding another option, but also arbitrarily complex, involving numerous options. In this study, we aim to understand how today's web configurators support users in their decision making process. In particular, we are interested in understanding how configurators handle decisions that are in conflict with dependencies. To abstract from different visualizations, we classify the existing strategies of web configurators and discuss advantages and disadvantages of them. While we identified eight strategies, a single configurator typically uses several of those strategies.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\TR78W9RU\\Thüm et al. - Product Conﬁguration in the Wild Strategies for C.pdf},
  langid = {english}
}

@article{tiihonenIntroductionPersonalizationMass2017,
  title = {An Introduction to Personalization and Mass Customization},
  author = {Tiihonen, Juha and Felfernig, Alexander},
  date = {2017-08},
  journaltitle = {Journal of Intelligent Information Systems},
  volume = {49},
  pages = {1--7},
  doi = {10.1007/s10844-017-0465-4},
  abstract = {Mass customization as a state-of-the-art production paradigm aims to produce individualized, highly variant products and services with nearly mass production costs. A major side-effect for companies providing complex products and services is that customers quite often get confused by the high variety and do not make a purchase. Personalization technologies can help to alleviate the challenges of mass customization. These technologies support customers in specifying products and services that fit their wishes and needs in a fashion where decision and interaction efforts with sales support systems are significantly reduced. We provide a short overview of related research and the articles that are part of this special issue on Personalization and Mass Customization.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\7LGAYLZB\\Tiihonen und Felfernig - 2017 - An introduction to personalization and mass custom.pdf},
  langid = {english},
  number = {1}
}

@online{TinyDB15Documentation,
  title = {{{TinyDB}} 3.15.1 Documentation},
  url = {https://tinydb.readthedocs.io/en/latest/},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\LL8TVTE9\\latest.html}
}

@book{tsangFoundationsConstraintSatisfaction1993,
  title = {Foundations of Constraint Satisfaction},
  author = {Tsang, Edward},
  date = {1993},
  publisher = {{Academic Press}},
  location = {{London}},
  isbn = {978-0-12-701610-8},
  langid = {english},
  note = {OCLC: 636781070},
  pagetotal = {421},
  series = {Computation in Cognitive Science}
}

@article{tsengApplyingCasebasedReasoning2005,
  title = {Applying Case-Based Reasoning for Product Configuration in Mass Customization Environments},
  author = {Tseng, Hwai-En and Chang, Chien-Chen and Chang, Shu-Hsuan},
  date = {2005-11-01},
  journaltitle = {Expert Systems with Applications},
  shortjournal = {Expert Systems with Applications},
  volume = {29},
  pages = {913--925},
  issn = {0957-4174},
  doi = {10.1016/j.eswa.2005.06.026},
  abstract = {Product variation and customization is a trend in current market-oriented manufacturing environment. Companies produce products in order to satisfy customer's needs. In the customization environment, the R\&D sector in an enterprise should be able to offer differentiation in product selection after they take the order. Such product differentiation should meet the requirement of cost and manufacturing procedure. In the light of this, how to generate an accurate bill of material (BOM) that meets the customer's needs and gets ready for the production is an important issue in the intensely competitive market. The purpose of this study is to reduce effectively the time and cost of design under the premise to manufacture an accurate new product. In this study, the Case-Based Reasoning (CBR) algorithm was used to construct the new BOM. Retrieving previous cases that resemble the current problem can save a lot of time in figuring out the problem and offer a correct direction for designers. When solving a new problem, CBR technique can quickly help generate a right BOM that fits the present situation.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\TQ6VF8TF\\Tseng et al_2005_Applying case-based reasoning for product configuration in mass customization.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\ZENJNEH5\\S0957417405001144.html},
  keywords = {Bill of material,Case-based reasoning,Feature tree,Mass-customization,Product configuration},
  langid = {english},
  number = {4}
}

@thesis{ullmannEntwurfUndUmsetzung2017,
  title = {Entwurf Und {{Umsetzung}} Einer {{Recommendation Engine}} Zur {{Produktkonfiguration}} Mit Maschinellen {{Lernverfahren}} Bei Der {{CAS Software AG}}},
  author = {Ullmann, Nils Merlin},
  date = {2017-07-27},
  institution = {{Hochschule Flensburg}},
  location = {{Flensburg}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\RT38GZN7\\Ullmann - Entwurf und Umsetzung einer Recommendation Engine .pdf},
  langid = {english},
  pagetotal = {77},
  type = {Bachelor's thesis}
}

@article{vanginkelKnowledgeDistributionInformation2009,
  title = {Knowledge about the Distribution of Information and Group Decision Making: {{When}} and Why Does It Work?},
  shorttitle = {Knowledge about the Distribution of Information and Group Decision Making},
  author = {van Ginkel, Wendy P. and van Knippenberg, Daan},
  date = {2009-03},
  journaltitle = {Organizational Behavior and Human Decision Processes},
  shortjournal = {Organizational Behavior and Human Decision Processes},
  volume = {108},
  pages = {218--229},
  issn = {07495978},
  doi = {10.1016/j.obhdp.2008.10.003},
  abstract = {Research has shown that decision-making groups with distributed information perform better when group members know which member is knowledgeable about what. Thus far research has been unable to identify the process responsible for this effect. In the present study, we propose that group members' task representations mediate the effect of knowledge about the distribution of information on decision performance. Building on this proposition, we also propose that reflection about the task moderates the effect of knowledge about distributed information through its effect on task representations. These hypotheses were put to the test in an experimental study of decision-making groups (N = 125). As predicted, knowledge of distributed information interacted with reflection to affect decision quality. Findings confirmed the proposed mediating role of task representations and information elaboration.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\Y4XVVAZ3\\van Ginkel und van Knippenberg - 2009 - Knowledge about the distribution of information an.pdf},
  langid = {english},
  number = {2},
  options = {useprefix=true}
}

@inproceedings{velasquez-guevaraMultiSPLOTSupportingMultiuser2018,
  title = {Multi-{{SPLOT}}: {{Supporting Multi}}-User {{Configurations}} with {{Constraint Programming}}},
  shorttitle = {Applied Informatics},
  author = {Vel\'asquez-Guevara, Sebastian and Pedraza, Gilberto and Chavarriaga, Jaime},
  date = {2018},
  volume = {942},
  pages = {364--378},
  publisher = {{Springer}},
  location = {{Cham}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5TSAW84B\\ICAI - 2018 - Applied informatics First International Conferenc.pdf},
  isbn = {978-3-030-01535-0 978-3-030-01534-3},
  langid = {english},
  series = {Communications in Computer and Information Science}
}

@inproceedings{velasquez-guevaraMultiSPLOTSupportingMultiuser2018a,
  title = {Multi-{{SPLOT}}: {{Supporting Multi}}-User {{Configurations}} with {{Constraint Programming}}},
  shorttitle = {Multi-{{SPLOT}}},
  booktitle = {Applied {{Informatics}}},
  author = {Vel\'asquez-Guevara, Sebastian and Pedraza, Gilberto and Chavarriaga, Jaime},
  date = {2018},
  pages = {364--378},
  publisher = {{Springer International Publishing}},
  abstract = {Nowadays, companies have moved from offering a single product for all their clients, to offer different customized for each one. These companies provide Configuration Systems where a user can decide and discard which features she wants in her final product. However, although almost all of these systems support individual decisions, usually they do not offer an special support for decisions made by multiple users for the same product. This paper introduces Multi-SPLOT, a web-based Configuration System that supports simultaneous decisions from multiple users. This system uses off-the-shelf solvers to determine if these decisions are not conflicting among them, and to propose solutions when the decisions of an user conflict with decisions of the others. This paper shows the design of the solution and details of its implementation using Angular, Firebase and the optimization library in Google App Script.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\G8E3GLZR\\Velásquez-Guevara et al. - 2018 - Multi-SPLOT Supporting Multi-user Configurations .pdf},
  isbn = {978-3-030-01535-0},
  keywords = {Configuration systems,Feature models,Multi-user configuration},
  langid = {english},
  series = {Communications in {{Computer}} and {{Information Science}}}
}

@inproceedings{wangEffectDefaultOptions2018,
  title = {The {{Effect}} of {{Default Options}} on {{Consumer Decisions}} in the {{Product Configuration Process}}},
  booktitle = {{{ConfWS}}},
  author = {Wang, Yue and Mo, Daniel Yiu-Wing},
  date = {2018-09},
  pages = {31--36},
  abstract = {Abstract. 1 Product configurators have been accepted as an important enabling toolkit to bridge customer needs and company offerings. In the configuration process, customers choose from a set of predefined attributes and their options. The combination of choices forms the desired product configuration. It is observed that some online configurators provide default options for each attribute. Although previous studies show that the default option significantly affects customers' choices during the product configuration process, it is not clear how other factors mediate this impact. In this paper, we investigate how product types, number of choices, customers' degree of expertise, the importance of the attributes and the configuring sequence affect consumers' decisions in the configuration process when default options are presented. Based on a series of empirical experiments, we find that customers' degree of expertise, the rating of the attribute importance, and the number of attribute choices have a significant effect on customers' choices for utilitarian products. For hedonic products, the importance of the attributes and the configuring sequence are significant factors.},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NCWNAUII\\Wang und Mo - The Effect of Default Options on Consumer Decision.pdf},
  langid = {english}
}

@thesis{wetzelPersonalisierterUndLernender2017,
  title = {Personalisierter und lernender Empfehlungsdienst f\"ur komplexe Produktkonfigurationen},
  author = {Wetzel, Gilles},
  date = {2017-10-30},
  institution = {{Hochschule Karlsruhe \textendash{} Technik und Wirtschaft}},
  location = {{Karlsruhe}},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GRXXLIXS\\Wetzel - Personalisierter und lernender Empfehlungsdienst f.pdf},
  langid = {german},
  pagetotal = {90},
  type = {Master's thesis}
}

@online{WhatWSGI,
  title = {What Is {{WSGI}}?},
  journaltitle = {WSGI.org},
  url = {https://wsgi.readthedocs.io/en/latest/what.html},
  urldate = {2020-04-09},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\77YX2NJ9\\what.html}
}

@article{winterInterviewMitAlan2009,
  title = {Interview mit Alan R. Hevner zum Thema ,,Design Science``},
  author = {Winter, Robert},
  date = {2009-02},
  journaltitle = {WIRTSCHAFTSINFORMATIK},
  shortjournal = {Wirtsch. Inform.},
  volume = {51},
  pages = {148--151},
  issn = {0937-6429, 1861-8936},
  doi = {10.1007/s11576-008-0109-y},
  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FZ8VR2JX\\Winter - 2009 - Interview mit Alan R. Hevner zum Thema „Design Sci.pdf},
  langid = {german},
  number = {1}
}


