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<fs x-large>**Recommendation Systems**</fs> \\
[[:book]]
====== Recommendation Systems  ======
===== 1 Introduction 1 =====
==== 1.1 Part I: Introduction to basic concepts 2 ====
==== 1.2 Part II: Recent developments 8 ====

====== PART I: INTRODUCTION TO BASIC CONCEPTS ======
===== 2 Collaborative recommendation 13 =====
==== 2.1 User-based nearest neighbor recommendation 13 ====
==== 2.2 Item-based nearest neighbor recommendation 18 ====
==== 2.3 About ratings 22 ====
==== 2.4 Further model-based and preprocessing-based approaches 26 ====
==== 2.5 Recent practical approaches and systems 40 ====
==== 2.6 Discussion and summary 47 ====
==== 2.7 Bibliographical notes 49 ====

===== 3 Content-based recommendation 51 ===== 
==== 3.1 Content representation and content similarity 52 ==== 
==== 3.2 Similarity-based retrieval 58 ==== 
==== 3.3 Other text classi?cation methods 63 ==== 
==== 3.4 Discussion 74 ==== 
==== 3.5 Summary 77 ==== 
==== 3.6 Bibliographical notes 79 ==== 

===== 4 Knowledge-based recommendation 81 ===== 
==== 4.1 Introduction 81 ==== 
==== 4.2 Knowledge representation and reasoning 82 ==== 
==== 4.3 Interacting with constraint-based recommenders 87 ==== 
==== 4.4 Interacting with case-based recommenders 101 ==== 
==== 4.5 Example applications 113 ==== 
==== 4.6 Bibliographical notes 122 ==== 

===== 5 Hybrid recommendation approaches 124 ===== 
==== 5.1 Opportunities for hybridization 125 ==== 
==== 5.2 Monolithic hybridization design 129 ==== 
==== 5.3 Parallelized hybridization design 134 ==== 
==== 5.4 Pipelined hybridization design 138 ==== 
==== 5.5 Discussion and summary 141 ==== 
==== 5.6 Bibliographical notes 142 ==== 

===== 6 Explanations in recommender systems 143 ===== 
==== 6.1 Introduction 143 ==== 
==== 6.2 Explanations in constraint-based recommenders 147 ==== 
==== 6.3 Explanations in case-based recommenders 157 ==== 
==== 6.4 Explanations in collaborative filtering recommenders 161 ==== 
==== 6.5 Summary 165 ==== 

===== 7 Evaluating recommender systems 166 ===== 
==== 7.1 Introduction 166 ==== 
==== 7.2 General properties of evaluation research 167 ==== 
==== 7.3 Popular evaluation designs 175 ==== 
==== 7.4 Evaluation on historical datasets 177 ==== 
==== 7.5 Alternate evaluation designs 184 ==== 
==== 7.6 Summary 187 ==== 
==== 7.7 Bibliographical notes 188 ==== 

===== 8 Case study: Personalized game recommendations on the mobile Internet 189 ===== 
==== 8.1 Application and personalization overview 191 ==== 
==== 8.2 Algorithms and ratings 193 ==== 
==== 8.3 Evaluation 194 ==== 
==== 8.4 Summary and conclusions 206 ==== 

====== PART II: RECENT DEVELOPMENTS ====== 

===== 9 Attacks on collaborative recommender systems 211 ===== 
==== 9.1 A first example 212 ==== 
==== 9.2 Attack dimensions 213 ==== 
==== 9.3 Attack types 214 ==== 
==== 9.4 Evaluation of effectiveness and countermeasures 219 ==== 
==== 9.5 Countermeasures 221 ==== 
==== 9.6 Privacy aspects distributed collaborative filtering 225 ==== 
==== 9.7 Discussion 232 ==== 

===== 10 Online consumer decision making 234 ===== 
==== 10.1 Introduction 234 ==== 
==== 10.2 Context effects 236 ==== 
==== 10.3 Primacy/recency effects 240 ==== 
==== 10.4 Further effects 243 ==== 
==== 10.5 Personality and social psychology 245 ==== 
==== 10.6 Bibliographical notes 252 ==== 

===== 11 Recommender systems and the next-generation web 253 ===== 
==== 11.1 Trust-aware recommender systems 254 ==== 
==== 11.2 Folksonomies and more 262 ==== 
==== 11.3 Ontological ?ltering 279 ==== 
==== 11.4 Extracting semantics from the web 285 ==== 
==== 11.5 Summary 288 ==== 

===== 12 Recommendations in ubiquitous environments 289 ===== 
==== 12.1 Introduction 289 ==== 
==== 12.2 Context-aware recommendation 291 ==== 
==== 12.3 Application domains 294 ==== 
==== 12.4 Summary 297 ==== 

===== 13 Summary and outlook 299 ===== 
==== 13.1 Summary 299 ==== 
==== 13.2 Outlook 300 ==== 

====== Bibliography 305 ======
====== Index 333 ======