update bib file

30_Thesis/thesis.bib

@@ -21,6 +21,19 @@
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\YXJISA5R\\Abdollahpouri et al_2019_Beyond Personalization.pdf}
 }
 
+@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}}},
@@ -100,6 +113,19 @@
   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},
@@ -145,6 +171,22 @@
   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},
@@ -233,25 +275,8 @@
   langid = {english}
 }
 
-@inproceedings{choudharyMulticriteriaGroupRecommender2020,
+@incollection{choudharyMulticriteriaGroupRecommender2020,
-  title = {Multi-Criteria {{Group Recommender System Based}} on {{Analytical Hierarchy Process}}},
+  ids = {choudharyMulticriteriaGroupRecommender2020},
-  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},
-  pages = {75--84},
-  publisher = {{Springer}},
-  location = {{Singapore}},
-  doi = {10.1007/978-981-13-8406-6_8},
-  abstract = {Current researches have demonstrated that the significance of Multi-Criteria 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 multi-criteria 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\\GCKZA5ZN\\2018_MA_Rubinshteyn_Hybrider Recommender für die Produktkonfiguration.pdf},
-  isbn = {9789811384066},
-  keywords = {Analytical hierarchy process,Decision-making,Multi-criteria group recommender systems,Recommendation mechanism},
-  langid = {english},
-  series = {Smart {{Innovation}}, {{Systems}} and {{Technologies}}}
-}
-
-@incollection{choudharyMulticriteriaGroupRecommender2020a,
   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.},
@@ -263,8 +288,9 @@
   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\\NN5RCJY2\\Choudhary und Bharadwaj - 2020 - Multi-criteria Group Recommender System Based on A.pdf},
+  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}
 }
 
@@ -278,6 +304,24 @@
   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},
@@ -315,6 +359,22 @@
   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},
@@ -398,7 +458,7 @@
   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},
+  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}
 }
 
@@ -474,6 +534,22 @@
   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}}},
@@ -552,6 +628,52 @@
   number = {2}
 }
 
+@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{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}
+}
+
+@article{hahslerRecommenderlabFrameworkDeveloping2015,
+  title = {Recommenderlab: {{A Framework}} for {{Developing}} and {{Testing Recommendation Algorithms}}},
+  shorttitle = {Recommenderlab},
+  author = {Hahsler, Michael},
+  date = {2015},
+  journaltitle = {https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf},
+  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;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NL8A54FQ\\Hahsler - recommenderlab A Framework for Developing and Tes.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\TAJZI98C\\1861.html},
+  langid = {english}
+}
+
 @article{hernandezdelolmoEvaluationRecommenderSystems2008,
   title = {Evaluation of Recommender Systems: {{A}} New Approach},
   shorttitle = {Evaluation of Recommender Systems},
@@ -717,7 +839,7 @@
   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},
+  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}
 }
 
@@ -742,6 +864,55 @@
   note = {ZSCC: 0000001}
 }
 
+@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}
+}
+
+@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},
@@ -904,6 +1075,48 @@
   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}
+}
+
+@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}
+}
+
 @article{qiuInfluenceGroupConfiguration2015,
   title = {Influence of Group Configuration on Online Discourse Reading},
   author = {Qiu, Mingzhu and McDougall, Douglas},
@@ -931,6 +1144,18 @@
   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},
@@ -946,6 +1171,19 @@ 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},
@@ -958,6 +1196,17 @@ OCLC: 935904837},
   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.},
@@ -983,6 +1232,37 @@ OCLC: 935904837},
   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},
@@ -1006,6 +1286,25 @@ OCLC: 935904837},
   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},
@@ -1023,6 +1322,16 @@ OCLC: 935904837},
   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}}},
@@ -1048,6 +1357,34 @@ OCLC: 935904837},
   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}}},
@@ -1086,6 +1423,23 @@ OCLC: 935904837},
   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},