finalize content foundation chapter

30_Thesis/thesis.bib

@@ -445,6 +445,7 @@
   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}}}
@@ -693,6 +694,23 @@
   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},
@@ -911,6 +929,23 @@ procedure.},
   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}}},