improve outline

25_Outline/outline.bib

@@ -1356,6 +1356,23 @@ 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},