add recommender sections to foundations

02_Proposal/proposal.bib

@@ -104,7 +104,6 @@
   publisher = {{Elsevier/MK, Morgan Kaufmann}},
   editor = {Felfernig, Alexander and Hotz, Lothar and Bagley, Claire and Tiihonen, Juha},
   year = {2014},
-  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\77SR5PQW\\Felfernig et al. - 2014 - Knowledge-based configuration from research to bu.pdf},
   note = {OCLC: 915548311}
 }
 
@@ -178,8 +177,6 @@
 @article{felfernigOpenConfiguration2014a,
   title = {Towards {{Open Configuration}}},
   language = {English},
-  urldate = {2019-10-15},
-  url = {https://researchportal.helsinki.fi/en/publications/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},
   year = {2014},
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NAFRZ496\\towards-open-configuration.html}
@@ -305,17 +302,6 @@ OCLC: 935904837}
   note = {ZSCC: 0000001}
 }
 
-@book{felfernigGroupRecommenderSystems2018a,
-  title = {Group Recommender Systems: An Introduction},
-  isbn = {978-3-319-75067-5},
-  shorttitle = {Group Recommender Systems},
-  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.},
-  language = {en},
-  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal{\v c}i{\v c}, Marko},
-  year = {2018},
-  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5AVYI9FS\\Felfernig et al. - 2018 - Group recommender systems an introduction.pdf}
-}
-
 @inproceedings{delgadoSimpleObjectivesWork2019,
   address = {{Copenhagen, Denmark}},
   title = {Simple {{Objectives Work Better}}},
@@ -381,7 +367,6 @@ OCLC: 935904837}
   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.},
   language = {en},
   booktitle = {Proceedings~of~ the~20th~{{International~Configuration~Workshop}}},
-  url = {http://confws.ist.tugraz.at},
   author = {Andrzejak, Artur and Friedrich, Gerhard and Wotawa, Franz},
   year = {2018},
   pages = {85-92},
@@ -809,10 +794,8 @@ OCLC: 935904837}
   isbn = {9789811384059 9789811384066},
   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.},
   language = {en},
-  urldate = {2019-11-04},
   booktitle = {Smart {{Systems}} and {{IoT}}: {{Innovations}} in {{Computing}}},
   publisher = {{Springer Singapore}},
-  url = {http://link.springer.com/10.1007/978-981-13-8406-6_8},
   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},
   year = {2020},
@@ -833,17 +816,6 @@ OCLC: 935904837}
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\YKJ2AIB9\\Ninaus et al. - INTELLIREQ Intelligent Techniques for Software Re.pdf}
 }
 
-@article{burke2002hybrid,
-  title = {Hybrid Recommender Systems: {{Survey}} and Experiments},
-  volume = {12},
-  number = {4},
-  journal = {User modeling and user-adapted interaction},
-  author = {Burke, Robin},
-  year = {2002},
-  pages = {331-370},
-  publisher = {{Springer}}
-}
-
 @article{article,
   title = {Collaborative Product Configuration: Formalization and Efficient Algorithms for Dependency Analysis.},
   volume = {3},
@@ -872,7 +844,6 @@ OCLC: 935904837}
 
 @misc{StudienUndPrufungsordnung2015,
   title = {Studien- Und {{Pr{\"u}fungsordnung}} Des {{Karlsruher Instituts}} F{\"u}r {{Technologie}} ({{KIT}}) F{\"u}r Den {{Bachelorstudiengang Informatik}}},
-  url = {https://www.informatik.kit.edu/downloads/info\%20bsc\%20spo\%202015.pdf},
   month = sep,
   year = {2015},
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\8TDXS8ES\\2015 - Studien- und Prüfungsordnung des Karlsruher Instit.pdf}
@@ -914,4 +885,46 @@ OCLC: 935904837}
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GRXXLIXS\\Wetzel - Personalisierter und lernender Empfehlungsdienst f.pdf}
 }
 
+@book{felfernigGroupRecommenderSystems2018b,
+  title = {Group {{Recommender Systems An Introduction}}},
+  isbn = {978-3-319-75067-5},
+  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.},
+  language = {English},
+  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal{\v c}i{\v c}, Marko},
+  year = {2018},
+  note = {OCLC: 1103789993}
+}
+
+@incollection{felfernigDecisionTasksBasic2018,
+  address = {{Cham}},
+  series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}},
+  title = {Decision {{Tasks}} and {{Basic Algorithms}}},
+  isbn = {978-3-319-75067-5},
+  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.},
+  language = {en},
+  booktitle = {Group {{Recommender Systems}} : {{An Introduction}}},
+  publisher = {{Springer International Publishing}},
+  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},
+  year = {2018},
+  pages = {3-26},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\745225S2\\Felfernig et al_2018_Decision Tasks and Basic Algorithms.pdf},
+  doi = {10.1007/978-3-319-75067-5_1}
+}
+
+@inproceedings{knijnenburgEachHisOwn2011,
+  address = {{Chicago, Illinois, USA}},
+  title = {Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems},
+  isbn = {978-1-4503-0683-6},
+  shorttitle = {Each to His Own},
+  language = {en},
+  booktitle = {Proceedings of the Fifth {{ACM}} Conference on {{Recommender}} Systems - {{RecSys}} '11},
+  publisher = {{ACM Press}},
+  doi = {10.1145/2043932.2043960},
+  author = {Knijnenburg, Bart P. and Reijmer, Niels J.M. and Willemsen, Martijn C.},
+  year = {2011},
+  pages = {141-148},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9MQQP8WF\\Knijnenburg et al_2011_Each to his own.pdf}
+}
+
 

02_Proposal/sections/10_foundations.tex

@@ -6,17 +6,39 @@
 
 A recommender system is a system that gives individualized recommendations to users to guide them through a large space of objects \cite[~ p. 331]{burkeHybridRecommenderSystems2002}.
 
+There are several approaches to recommender systems presented in \cite{felfernigGroupRecommenderSystems2018}, these are: collaborative filtering, content-based filtering, critiquing-based filtering, constraint-based, hybrid recommendation.
+
+\subsection{Collaborative Filtering}
+In collaborative filtering a users rating for unknown items is predicted by finding similar users who have rated it. Their rating is used as prediction
+\cite[~ pp. 7, 8]{felfernigDecisionTasksBasic2018}.
+
+\subsection{Content-Based Filtering}
+Items and users are assigned to categories. Based on consumption and rating of items a user will have implicit ratings for categories. Predictions are now made based on a categories of the new item \cite[~ pp. 10, 11]{felfernigDecisionTasksBasic2018}.
+
+\subsection{Critiquing-Based Recommendation}
+Items are recommended and a user can then critique on attributes of the recommendation. Based on that a similar item which does not have those critiques can be recommended. User preferences are implicitly collected this way \cite{knijnenburgEachHisOwn2011}.
+
+\subsection{Constraint-Based Recommendation}
+Hereby filter rules are defined which filter out items that don't fulfil specified rules. A user models their requirements with these rules and thereby gets a list of recommended items \cite[~ p. 12]{felfernigDecisionTasksBasic2018}
+
+\subsection{Hybrid Recommendation}
+A hybrid recommender combines different recommendation approaches to use the strengths of each individual one and to reduce effects of weaknesses \cite{burkeHybridRecommenderSystems2002}.
+
+\section{Group Recommender System}
+
+A group recommender system is a recommender system aimed at making recommendations for a group instead of a single user. To make recommendations group members preferences have to be aggregated. This can be done by either aggregating single user recommendations or by merging preferences of each user into a group preference model. Based on this model recommendation strategies as described in \ref{sec:Foundations:RecommenderSystem} can be used to generate recommendations.
+%TODO: citations for group recommender system
 
 \section{Product Configuration}
 \label{sec:Foundations:ProductConfiguration}
 
-Product configuration is a process consisting of a series of decision tasks whereby a product is constructed of components which interact with each other. During a configuration process no new components are created. Their interplay and specification is defined beforehand \cite[~ p. 42, 43]{sabinProductConfigurationFrameworksa1998}.
+Product configuration is a process consisting of a series of decision tasks whereby a product is constructed of components which interact with each other. During a configuration process no new components are created. Their interplay and specification is defined beforehand \cite[~ pp. 42, 43]{sabinProductConfigurationFrameworksa1998}.
 
 Formally a configuration problem can be specified as a \emph{constraint satisfaction problem (CSP)} \cite{tsangFoundationsConstraintSatisfaction1993} as 
 \[
     CSP(V,D,C)
 \]
-where \( V = \{v_1,\dots, v_n\} \) is a set of variables, \( D = dom : V \mapsto X \) is a relation of variables and their corresponding domain definitions \( X \), and \( C = C_{PREF} \cup C_{KB} \) is a set of constraints with customer preferences \( C_{PREF} \) and configuration knowledge base \( C_{KB} \) \cite{felfernigOpenConfiguration2014, felferningGroupBasedConfiguration2016}.
+where \( V = \{v_1,\dots, v_n\} \) is a set of variables, \( D = dom : V \mapsto X \) is a relation of variables and their corresponding domain definitions \( X \), and \( C = C_{PREF} \cup C_{KB} \) is a set of constraints with customer preferences \( C_{PREF} \) and configuration knowledge base \( C_{KB} \) \cite{felferningGroupBasedConfiguration2016, felfernigOpenConfiguration2014}.
 
 
 \section{Group-Based Product Configuration}