add some sources to comparison table

25_Outline/outline.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}}},
@@ -290,6 +303,24 @@
   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},
@@ -596,6 +627,52 @@
   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},
@@ -786,6 +863,38 @@
   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}
+}
+
+@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},
@@ -948,6 +1057,48 @@
   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},
@@ -1027,6 +1178,17 @@ 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.},
@@ -1116,6 +1278,15 @@ OCLC: 935904837},
   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},
@@ -1168,6 +1339,17 @@ OCLC: 935904837},
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\8TDXS8ES\\2015 - Studien- und Prüfungsordnung des Karlsruher Instit.pdf}
 }
 
+@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}}},
@@ -1206,6 +1388,23 @@ 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},

25_Outline/sections/20_foundations.tex

@@ -257,7 +257,7 @@ Using our example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferenc
                     \item No results
                 \end{itemize} \\ 
         \end{tabularx}
-        \caption{A description of the advantages and disadvantages of common recommendation techniques}
+        \caption{A description of the advantages and disadvantages of common recommendation techniques \cite{richthammerSituationAwarenessRecommender2018, shokeenStudyFeaturesSocial2019,hahslerRecommenderlabFrameworkDeveloping2015, aminiDiscoveringImpactKnowledge2011}}
         \label{tab:Foundations:RecommenderComparison}
     \end{center}
 \end{table}