**Recommendation Systems** \\ [[: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 ======