Preface	6
	Contents	8
	List of Contributors	1
	Chapter 1 Introduction to Recommender Systems Handbook	28
	1.1 Introduction	28
	1.2 Recommender Systems Function	31
	1.3 Data and Knowledge Sources	34
	1.4 Recommendation Techniques	37
	1.5 Application and Evaluation	41
	1.6 Recommender Systems and Human Computer Interaction	44
	1.6.1 Trust, Explanations and Persuasiveness	45
	1.6.2 Conversational Systems	46
	1.6.3 Visualization	48
	1.7 Recommender Systems as a Multi-Disciplinary Field	48
	1.8 Emerging Topics and Challenges	50
	1.8.1 Emerging Topics Discussed in the Handbook	50
	1.8.2 Challenges	53
	References	56
	Part IBasic Techniques	63
	Chapter 2 Data Mining Methods for RecommenderSystems	64
	2.1 Introduction	64
	2.2 Data Preprocessing	65
	2.2.1 Similarity Measures	66
	2.2.2 Sampling	67
	2.2.3 Reducing Dimensionality	69
	2.2.3.1 Principal Component Analysis	69
	2.2.3.2 Singular Value Decomposition	70
	2.2.4 Denoising	72
	2.3 Classification	73
	2.3.1 Nearest Neighbors	73
	2.3.2 Decision Trees	75
	2.3.3 Ruled-based Classifiers	76
	2.3.4 Bayesian Classifiers	77
	2.3.5 Artificial Neural Networks	79
	2.3.6 Support Vector Machines	81
	2.3.7 Ensembles of Classifiers	83
	2.3.8 Evaluating Classifiers	84
	2.4 Cluster Analysis	86
	2.4.1 k-Means	87
	2.4.2 Alternatives to k-means	88
	2.5 Association Rule Mining	89
	2.6 Conclusions	91
	Acknowledgments	92
	References	92
	Chapter 3 Content-based Recommender Systems: State of the Art and Trends	97
	3.1 Introduction	98
	3.2 Basics of Content-based Recommender Systems	99
	3.2.1 A High Level Architecture of Content-based Systems	99
	3.2.2 Advantages and Drawbacks of Content-based Filtering	102
	3.3 State of the Art of Content-based Recommender Systems	103
	3.3.1 Item Representation	104
	3.3.1.1 Keyword-based Vector Space Model	105
	3.3.1.2 Review of Keyword-based Systems	106
	3.3.1.3 Semantic Analysis by using Ontologies	109
	3.3.1.4 Semantic Analysis by using Encyclopedic Knowledge Sources	112
	3.3.2 Methods for Learning User Profiles	114
	3.3.2.1 Probabilistic Methods and Na¨?ve Bayes	115
	3.3.2.2 Relevance Feedback and Rocchio’s Algorithm	116
	3.3.2.3 Other Methods	117
	3.4 Trends and Future Research	118
	3.4.1 The Role of User Generated Content in the Recommendation Process	118
	3.4.1.1 Social Tagging Recommender Systems	119
	3.4.2 Beyond Over-specializion: Serendipity	120
	3.5 Conclusions	123
	References	124
	Chapter 4 A Comprehensive Survey of Neighborhood-based Recommendation Methods	130
	4.1 Introduction	130
	4.1.1 Formal Definition of the Problem	131
	4.1.2 Overview of Recommendation Approaches	133
	4.1.2.1 Content-based approaches	133
	4.1.2.2 Collaborative filtering approaches	134
	4.1.3 Advantages of Neighborhood Approaches	135
	4.1.4 Objectives and Outline	136
	4.2 Neighborhood-based Recommendation	137
	4.2.1 User-based Rating Prediction	138
	4.2.2 User-based Classification	139
	4.2.3 Regression VS Classification	140
	4.2.4 Item-based Recommendation	140
	4.2.5 User-based VS Item-based Recommendation	141
	4.3 Components of Neighborhood Methods	143
	4.3.1 Rating Normalization	144
	4.3.1.1 Mean-centering	144
	4.3.1.2 Z-score normalization	145
	4.3.1.3 Choosing a normalization scheme	146
	4.3.2 Similarity Weight Computation	147
	4.3.2.1 Correlation-based similarity	147
	4.3.2.2 Other similarity measures	148
	4.3.2.3 Accounting for significance	150
	4.3.2.4 Accounting for variance	151
	4.3.3 Neighborhood Selection	152
	4.3.3.1 Pre-filtering of neighbors	152
	4.3.3.2 Neighbors in the predictions	153
	4.4 Advanced Techniques	154
	4.4.1 Dimensionality Reduction Methods	155
	4.4.1.1 Decomposing the rating matrix	155
	4.4.1.2 Decomposing the similarity matrix	157
	4.4.2 Graph-based Methods	158
	4.4.2.1 Path-based similarity	159
	4.4.2.2 Random walk similarity	160
	4.5 Conclusion	162
	References	163
	Chapter 5Advances in Collaborative Filtering	168
	5.1 Introduction	168
	5.2 Preliminaries	170
	5.2.1 Baseline predictors	171
	5.2.2 The Netflix data	172
	5.2.3 Implicit feedback	173
	5.3 Matrix factorization models	174
	5.3.1 SVD	174
	5.3.2 SVD++	176
	5.3.3 Time-aware factor model	177
	5.3.3.1 Time changing baseline predictors	177
	5.3.3.2 Time changing factor model	181
	5.3.4 Comparison	182
	5.3.4.1 Predicting future days	183
	5.3.5 Summary	183
	5.4 Neighborhood models	184
	5.4.1 Similarity measures	185
	5.4.2 Similarity-based interpolation	186
	5.4.3 Jointly derived interpolation weights	188
	5.4.3.1 Formal model	188
	5.4.3.2 Computational issues	190
	5.4.4 Summary	191
	5.5 Enriching neighborhood models	191
	5.5.1 A global neighborhood model	192
	5.5.1.1 Building the model	192
	5.5.1.2 Parameter Estimation	194
	5.5.1.3 Comparison of accuracy	195
	5.5.2 A factorized neighborhood model	196
	5.5.2.1 Factoring item-item relationships	197
	5.5.2.2 A user-user model	200
	5.5.3 Temporal dynamics at neighborhood models	203
	5.5.4 Summary	205
	5.6 Between neighborhood and factorization	205
	References	207
	Chapter 6Developing Constraint-based Recommenders	210
	6.1 Introduction	210
	6.2 Development of Recommender Knowledge Bases	214
	6.3 User Guidance in Recommendation Processes	217
	6.4 Calculating Recommendations	226
	6.5 Experiences from Projects and Case Studies	228
	6.6 Future Research Issues	230
	6.7 Summary	235
	References	235
	Chapter 7Context-Aware Recommender Systems	239
	7.1 Introduction and Motivation	240
	7.2 Context in Recommender Systems	241
	7.2.1 What is Context?	241
	7.2.2 Modeling Contextual Information in Recommender Systems	245
	7.2.3 Obtaining Contextual Information	250
	7.3 Paradigms for Incorporating Context in Recommender Systems	252
	7.3.1 Contextual Pre-Filtering	255
	7.3.2 Contextual Post-Filtering	259
	7.3.3 Contextual Modeling	260
	7.3.3.1 Heuristic-Based Approaches	261
	7.3.3.2 Model-Based Approaches	262
	7.4 Combining Multiple Approaches	265
	7.4.1 Case Study of Combining Multiple Pre-Filters: Algorithms	266
	7.4.2 Case Study of Combining Multiple Pre-Filters: ExperimentalResults	267
	7.5 Additional Issues in Context-Aware Recommender Systems	269
	7.6 Conclusions	271
	Acknowledgements	272
	References	272
	Part IIApplications and Evaluation of RSs	276
	Chapter 8Evaluating Recommendation Systems	277
	8.1 Introduction	278
	8.2 Experimental Settings	280
	8.2.1 Offline Experiments	281
	8.2.1.1 Data sets for offline experiments	281
	8.2.1.2 Simulating user behavior	282
	8.2.1.3 More complex user modeling	283
	8.2.2 User Studies	283
	8.2.2.1 Advantages and Disadvantages	284
	8.2.2.2 Between vs.Within Subjects	285
	8.2.2.3 Variable Counter Balance	286
	8.2.2.4 Questionnaires	286
	8.2.3 Online Evaluation	286
	8.2.4 Drawing Reliable Conclusions	287
	8.2.4.1 Confidence and p-values	288
	8.2.4.2 Paired Results	288
	8.2.4.3 Unpaired Results	289
	8.2.4.4 Multiple tests	290
	8.2.4.5 Confidence Intervals	291
	8.3 Recommendation System Properties	291
	8.3.1 User Preference	292
	8.3.2 Prediction Accuracy	293
	8.3.2.1 Measuring Ratings Prediction Accuracy	293
	8.3.2.2 Measuring Usage Prediction	294
	8.3.2.3 Ranking Measures	297
	8.3.3 Coverage	301
	8.3.3.1 Item Space Coverage	302
	8.3.3.2 User Space Coverage	302
	8.3.3.3 Cold Start	303
	8.3.4 Confidence	303
	8.3.5 Trust	305
	8.3.6 Novelty	305
	8.3.7 Serendipity	306
	8.3.8 Diversity	308
	8.3.9 Utility	309
	8.3.10 Risk	310
	8.3.11 Robustness	310
	8.3.12 Privacy	311
	8.3.13 Adaptivity	312
	8.3.14 Scalability	313
	8.4 Conclusion	313
	References	314
	Chapter 9 A Recommender System for an IPTV Service Provider: a Real Large-Scale ProductionEnvironment	318
	9.1 Introduction	318
	9.2 IPTV Architecture	320
	9.2.1 IPTV Search Problems	321
	9.3 Recommender System Architecture	322
	9.3.1 Data Collection	323
	9.3.2 Batch and Real-Time Stages	325
	9.4 Recommender Algorithms	327
	9.4.1 Overview of Recommender Algorithms	327
	9.4.2 LSA Content-Based Algorithm	330
	9.4.3 Item-based Collaborative Algorithm	333
	9.4.4 Dimensionality-Reduction-Based Collaborative Algorithm	335
	9.5 Recommender Services	337
	9.6 System Evaluation	338
	9.6.1 Off-Line Analysis	340
	9.6.2 On-line Analysis	344
	9.7 Conclusions	348
	References	348
	Chapter 10How to Get the Recommender Out of the Lab?	351
	10.1 Introduction	352
	10.2 Designing Real-World Recommender Systems	352
	10.3 Understanding the Recommender Environment	353
	10.3.1 Application Model	353
	10.3.1.1 Understanding the recommender role in the application	354
	10.3.1.2 Understanding the influence of the application implementation	357
	10.3.2 User Model	358
	10.3.2.1 Understanding who the users are	359
	10.3.2.2 Understanding users’ motivations, goals and expectations	360
	10.3.2.3 Understanding users’ context	361
	10.3.3 Data Model	362
	10.3.3.1 Understanding the type of available data to describe items	363
	10.3.3.2 Understanding the quality / quantity of metadata	364
	10.3.3.3 Understanding the properties of the item set	366
	10.3.4 A Method for Using Environment Models	367
	10.4 Understanding the Recommender Validation Steps in anIterative Design Process	368
	10.4.1 Validation of the Algorithms	368
	10.4.2 Validation of the Recommendations	369
	10.4.2.1 Card Sorting	369
	10.4.2.2 Low fidelity prototyping	370
	10.4.2.3 Subjective qualitative evaluation	371
	10.4.2.4 Diary studies	372
	10.5 Use Case: a Semantic News Recommendation System	373
	10.5.1 Context: MESH Project	374
	10.5.2 Environmental Models in MESH	375
	10.5.2.1 Instantiation of the environmental models	375
	10.5.2.2 Links between the different models and constraints on design	375
	10.5.3 In Practice: Iterative Instantiations of Models	379
	10.6 Conclusion	380
	References	380
	Chapter 11Matching Recommendation Technologies andDomains	384
	11.1 Introduction	384
	11.2 RelatedWork	385
	11.3 Knowledge Sources	385
	11.3.1 Recommendation types	387
	11.4 Domain	389
	11.4.1 Heterogeneity	389
	11.4.2 Risk	390
	11.4.3 Churn	390
	11.4.4 Interaction Style	391
	11.4.5 Preference stability	391
	11.4.6 Scrutability	392
	11.5 Knowledge Sources	392
	11.5.1 Social Knowledge	392
	11.5.2 Individual	393
	11.5.3 Content	394
	11.5.3.1 Domain Knowledge	394
	11.6 Mapping Domains to Technologies	395
	11.6.1 Algorithms	397
	11.6.2 Sample Recommendation Domains	398
	11.7 Conclusion	399
	Acknowledgements	399
	References	399
	Chapter 12 Recommender Systems in Technology EnhancedLearning	404
	12.1 Introduction	405
	12.2 Background	406
	12.3 RelatedWork	409
	12.4 Survey of TEL Recommender Systems	416
	12.5 Evaluation of TEL Recommenders	421
	12.6 Conclusions and further work	425
	Acknowledgements	426
	References	426
	Part IIIInteracting with Recommender Systems	433
	Chapter 13On the Evolution of Critiquing Recommenders	434
	13.1 Introduction	434
	13.2 The Early Days: Critiquing Systems/Recognised Benefits	435
	13.3 Representation & Retrieval Challenges for CritiquingSystems	437
	13.3.1 Approaches to Critique Representation	437
	13.3.1.1 Over-critiquing & protracted recommendation dialogues	437
	13.3.1.2 Critique redundancy & hidden feature-dependency	439
	13.3.1.3 Limited product-space vision	439
	13.3.1.4 Weak relevance of presented feedback options	441
	13.3.1.5 Limitations of domain & preference driven approaches	441
	13.3.1.6 Restricted user control	442
	13.3.2 Retrieval Challenges in Critique-Based Recommenders	445
	13.3.2.1 Preference inconsistency and longevity	446
	13.3.2.2 Diminishing choices & unreachability	447
	13.3.2.3 Refining recommendation retrievals	450
	13.3.2.4 Multi-user preference handing	451
	13.4 Interfacing Considerations Across Critiquing Platforms	453
	13.4.1 Scaling to Alternate Critiquing Platforms	453
	13.4.2 Direct Manipulation Interfaces vs Restricted User Control	455
	13.4.3 Supporting Explanation, Confidence & Trust	456
	13.4.4 Visualisation, Adaptivity, and Partitioned Dynamicity	458
	13.4.5 Respecting Multi-cultural Usability Differences	460
	13.5 Evaluating Critiquing: Resources, Methodologies andCriteria	460
	13.5.1 Resources & Methodologies	461
	13.5.2 Evaluation Criteria	461
	13.6 Conclusion / Open Challenges & Opportunities	463
	References	464
	Chapter 14 Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender SystemEvaluations	469
	14.1 Introduction	469
	14.2 Recommender Systems as Social Actors	470
	14.3 Source Credibility	471
	14.3.1 Trustworthiness	472
	14.3.2 Expertise	472
	14.3.3 Influences on Source Credibility	472
	14.4 Source Characteristics Studied in Human-HumanInteractions	473
	14.4.1 Similarity	473
	14.4.1.1 Expertise Judgments	473
	14.4.1.2 Trustworthiness Judgments	473
	14.4.2 Likeability	474
	14.4.3 Symbols of Authority	474
	14.4.4 Styles of Speech	475
	14.4.5 Physical Attractiveness	475
	14.4.6 Humor	475
	14.5 Source Characteristics in Human-Computer Interactions	476
	14.6 Source Characteristics in Human-Recommender SystemInteractions	477
	14.6.1 Recommender system type	477
	14.6.2 Input characteristics	478
	14.6.3 Process characteristics	479
	14.6.4 Output characteristics	479
	14.6.5 Characteristics of embodied agents	481
	14.7 Discussion	482
	14.8 Implications	482
	14.9 Directions for future research	484
	References	485
	Chapter 15 Designing and Evaluating Explanations forRecommender Systems	492
	15.1 Introduction	492
	15.2 Guidelines	494
	15.3 Explanations in Expert Systems	494
	15.4 Defining Goals	495
	15.4.1 Explain How the System Works: Transparency	496
	15.4.2 Allow Users to Tell the System it is Wrong: Scrutability	498
	15.4.3 Increase Users’ Confidence in the System: Trust	498
	15.4.4 Convince Users to Try or Buy: Persuasiveness	500
	15.4.5 Help Users Make Good Decisions: Effectiveness	501
	15.4.6 Help Users Make Decisions Faster: Efficiency	503
	15.4.7 Make the use of the system enjoyable: Satisfaction	504
	15.5 Evaluating the Impact of Explanations on theRecommender System	505
	15.5.1 Accuracy Metrics	506
	15.5.2 Learning Rate	506
	15.5.3 Coverage	507
	15.5.4 Acceptance	507
	15.6 Designing the Presentation and Interaction withRecommendations	508
	15.6.1 Presenting Recommendations	508
	15.6.2 Interacting with the Recommender System	509
	15.7 Explanation Styles	510
	15.7.1 Collaborative-Based Style Explanations	513
	15.7.2 Content-Based Style Explanation	514
	15.7.3 Case-Based Reasoning (CBR) Style Explanations	516
	15.7.4 Knowledge and Utility-Based Style Explanations	517
	15.7.5 Demographic Style Explanations	518
	15.8 Summary and future directions	518
	References	520
	Chapter 16Usability Guidelines for Product Recommenders Based on Example Critiquing Research	524
	16.1 Introduction	525
	16.2 Preliminaries	526
	16.2.1 Interaction Model	526
	16.2.2 Utility-Based Recommenders	528
	16.2.3 The Accuracy, Confidence, Effort Framework	530
	16.2.4 Organization of this Chapter	531
	16.3 RelatedWork	531
	16.3.1 Types of Recommenders	531
	16.3.2 Rating-based Systems	532
	16.3.3 Case-based Systems	532
	16.3.4 Utility-based Systems	532
	16.3.5 Critiquing-based Systems	533
	16.3.6 Other Design Guidelines	533
	16.4 Initial Preference Elicitation	534
	16.5 Stimulating Preference Expression with Examples	538
	16.5.1 How Many Examples to Show	540
	16.5.2 What Examples to Show	540
	16.6 Preference Revision	543
	16.6.1 Preference Conflicts and Partial Satisfaction	544
	16.6.2 Tradeoff Assistance	545
	16.7 Display Strategies	547
	16.7.1 Recommending One Item at a Time	547
	16.7.2 Recommending K best Items	548
	16.7.3 Explanation Interfaces	549
	16.8 A Model for Rationalizing the Guidelines	550
	16.9 Conclusion	554
	References	554
	Chapter 17 Map Based Visualization of Product Catalogs	559
	17.1 Introduction	559
	17.2 Methods for Map Based Visualization	561
	17.2.1 Self-Organizing Maps	562
	17.2.2 Treemaps	563
	17.2.3 Multidimensional Scaling	565
	17.2.4 Nonlinear Principal Components Analysis	565
	17.3 Product Catalog Maps	566
	17.3.1 Multidimensional Scaling	567
	17.3.2 Nonlinear Principal Components Analysis	570
	17.4 Determining AttributeWeights using Clickstream Analysis	571
	17.4.1 Poisson Regression Model	572
	17.4.2 Handling Missing Values	572
	17.4.3 Choosing Weights Using Poisson Regression	573
	17.4.4 Stepwise Poisson Regression Model	574
	17.5 Graphical Shopping Interface	574
	17.6 E-Commerce Applications	575
	17.6.1 MDS Based Product Catalog Map Using Attribute Weights	576
	17.6.2 NL-PCA Based Product Catalog Map	580
	17.6.3 Graphical Shopping Interface	582
	17.7 Conclusions and Outlook	585
	Acknowledgements	586
	References	586
	Part IVRecommender Systems and Communities	589
	Chapter 18 Communities, Collaboration, and RecommenderSystems in PersonalizedWeb Search	590
	18.1 Introduction	590
	18.2 A Brief History ofWeb Search	592
	18.3 The Future ofWeb Search	594
	18.3.1 Personalized Web Search	595
	18.3.2 Collaborative Information Retrieval	599
	18.3.3 Towards Social Search	601
	18.4 Case-Study 1 - Community-BasedWeb Search	602
	18.4.1 Repetition and Regularity in Search Communities	603
	18.4.2 The Collaborative Web Search System	604
	18.4.3 Evaluation	607
	18.4.4 Discussion	609
	18.5 Case-Study 2 - Web Search. Shared.	609
	18.5.1 The HeyStaks System	610
	18.5.2 The HeyStaks Recomendation Engine	613
	18.5.3 Evaluation	615
	18.5.4 Discussion	618
	18.6 Conclusions	618
	Acknowledgements	619
	References	620
	Chapter 19Social Tagging Recommender Systems	626
	19.1 Introduction	627
	19.2 Social Tagging Recommenders Systems	628
	19.2.1 Folksonomy	629
	19.2.2 The Traditional Recommender Systems Paradigm	630
	19.2.3 Multi-mode Recommendations	631
	19.3 RealWorld Social Tagging Recommender Systems	632
	19.3.1 What are the Challenges?	632
	19.3.2 BibSonomy as Study Case	633
	19.3.2.1 System Description	633
	19.3.2.2 Recommendations in BibSonom	633
	19.3.2.3 Technological and Infrastructure Requirements	634
	19.3.3 Tag Acquisition	635
	19.4 Recommendation Algorithms for Social Tagging Systems	637
	19.4.1 Collaborative Filtering	637
	19.4.2 Recommendation based on Ranking	641
	19.4.2.1 Ranking based on Tensor Factorization	641
	19.4.2.2 FolkRank	643
	19.4.3 Content-Based Social Tagging RS	645
	19.4.3.1 Text-Based	645
	19.4.3.2 Image-Based	646
	19.4.3.3 Audio-Based	647
	19.4.4 Evaluation Protocols and Metrics	648
	19.5 Comparison of Algorithms	650
	19.6 Conclusions and Research Directions	651
	References	653
	Chapter 20Trust and Recommendations	656
	20.1 Introduction	656
	20.2 Computational Trust	658
	20.2.1 Trust Representation	659
	20.2.2 Trust Computation	661
	20.2.2.1 Propagation	661
	20.2.2.2 Aggregation	665
	20.3 Trust-Enhanced Recommender Systems	666
	20.3.1 Motivation	667
	20.3.2 State of the Art	669
	20.3.2.1 Mining a Trust Network	670
	20.3.2.2 Automatic Trust Generation	673
	20.3.3 Empirical Comparison	675
	20.3.3.1 Data Sets	675
	20.3.3.2 Coverage	677
	20.3.3.3 Accuracy	679
	20.3.3.4 Conclusion	680
	20.4 Recent Developments and Open Challenges	681
	20.5 Conclusions	683
	References	683
	Chapter 21Group Recommender Systems:Combining Individual Models	687
	21.1 Introduction	687
	21.2 Usage Scenarios and Classification of GroupRecommenders	689
	21.2.1 Interactive Television	689
	21.2.2 Ambient Intelligence	689
	21.2.3 Scenarios Underlying Related Work	690
	21.2.4 A Classification of Group Recommenders	691
	21.3 Aggregation Strategies	692
	21.3.1 Overview of Aggregation Strategies	692
	21.3.2 Aggregation Strategies Used in Related Work	693
	21.3.3 Which Strategy Performs Best	695
	21.4 Impact of Sequence Order	696
	21.5 Modelling Affective State	698
	21.5.1 Modelling an Individual’s Satisfaction on its Own	699
	21.5.2 Effects of the Group on an Individual’s Satisfaction	700
	21.6 Using Affective State inside Aggregation Strategies	701
	21.7 Applying Group Recommendation to Individual Users	703
	21.7.1 Multiple Criteria	703
	21.7.2 Cold-Start Problem	705
	21.7.3 Virtual Group Members	707
	21.8 Conclusions and Challenges	707
	21.8.1 Main Issues Raised	707
	21.8.2 Caveat: Group Modelling	708
	21.8.3 Challenges	708
	Acknowledgments	711
	References	711
	Part VAdvanced Algorithms	713
	Chapter 22 Aggregation of Preferences in RecommenderSystems	714
	22.1 Introduction	714
	22.2 Types of Aggregation in Recommender Systems	715
	22.2.1 Aggregation of Preferences in CF	717
	22.2.2 Aggregation of Features in CB and UB Recommendation	717
	22.2.3 Profile Construction for CB, UB	718
	22.2.4 Item and User Similarity and Neighborhood Formation	718
	22.2.5 Connectives in Case-Based Reasoning for RS	720
	22.2.6 Weighted Hybrid Systems	720
	22.3 Review of Aggregation Functions	721
	22.3.1 Definitions and Properties	721
	22.3.1.1 Practical Considerations in RS	723
	22.3.2 Aggregation Families	725
	22.3.2.1 Quasi-Arithmetic Means	725
	22.3.2.2 OWA Functions	726
	22.3.2.3 Choquet and Sugeno integrals	727
	22.3.2.4 T-Norms and T-Conorms	729
	22.3.2.5 Nullnorms and Uninorms	730
	22.4 Construction of Aggregation Functions	731
	22.4.1 Data Collection and Preprocessing	731
	22.4.2 Desired Properties, Semantics and Interpretation	733
	22.4.3 Complexity and the Understanding of Function Behavior	734
	22.4.4 Weight and Parameter Determination	735
	22.5 Sophisticated Aggregation Procedures in RecommenderSystems: Tailoring for Specific Applications	735
	22.6 Conclusions	740
	22.7 Further Reading	741
	Acknowledgements	741
	References	742
	Chapter 23Active Learning in Recommender Systems	744
	23.1 Introduction	744
	23.1.1 Objectives of Active Learning in Recommender Systems	746
	23.1.2 An Illustrative Example	747
	23.1.3 Types of Active Learning	748
	23.2 Properties of Data Points	749
	23.2.1 Other Considerations	750
	23.3 Active Learning in Recommender Systems	751
	23.3.1 Method Summary Matrix	751
	23.4 Active Learning Formulation	751
	23.5 Uncertainty-based Active Learning	755
	23.5.1 Output Uncertainty	755
	23.5.1.1 Active Learning Methods	756
	23.5.1.2 Uncertainty Measurement	756
	23.5.2 Decision Boundary Uncertainty	757
	23.5.3 Model Uncertainty	758
	23.5.3.1 Probabilistic Models	758
	23.6 Error-based Active Learning	760
	23.6.1 Instance-based Methods	761
	23.6.1.1 Output Estimates Change (Y-Change)	761
	23.6.1.2 Cross Validation-based	762
	23.6.2 Model-based	763
	23.6.2.1 Parameter Change-based	764
	23.6.2.2 Variance-based	764
	23.6.2.3 Image Restoration-based	765
	23.7 Ensemble-based Active Learning	765
	23.7.1 Models-based	765
	23.7.2 Candidates-based	766
	23.8 Conversation-based Active Learning	769
	23.8.1 Case-based Critique	770
	23.8.2 Diversity-based	770
	23.8.3 Query Editing-based	771
	23.9 Computational Considerations	771
	23.10 Discussion	772
	Acknowledgments	773
	References	773
	Chapter 24Multi-Criteria Recommender Systems	777
	24.1 Introduction	777
	24.2 Recommendation as a Multi-Criteria Decision MakingProblem	779
	24.2.1 Object of Decision	780
	24.2.2 Family of Criteria	781
	24.2.3 Global Preference Model	782
	24.2.4 Decision Support Process	783
	24.3 MCDM Framework for Recommender Systems: LessonsLearned	784
	24.4 Multi-Criteria Rating Recommendation	788
	24.4.1 Traditional single-rating recommendation problem	789
	24.4.2 Extending traditional recommender systems to includemulti-criteria ratings	790
	24.5 Survey of Algorithms for Multi-Criteria RatingRecommenders	791
	24.5.1 Engaging Multi-Criteria Ratings during Prediction	792
	24.5.1.1 Heuristic approaches	792
	24.5.1.2 Model-based approaches	795
	24.5.2 Engaging Multi-Criteria Ratings during Recommendation	799
	24.5.2.1 Related work: multi-criteria optimization	800
	24.5.2.2 Designing a total order for item recommendations	800
	24.5.2.3 Finding Pareto optimal item recommendations	801
	24.5.2.4 Using multi-criteria ratings as recommendation filters	802
	24.6 Discussion and FutureWork	803
	24.7 Conclusions	805
	References	806
	Chapter 25 Robust Collaborative Recommendation	812
	25.1 Introduction	812
	25.2 Defining the Problem	814
	25.2.1 An Example Attack	816
	25.3 Characterising Attacks	817
	25.3.1 Basic Attacks	817
	25.3.1.1 Random Attack	817
	25.3.1.2 Average Attack	817
	25.3.2 Low-knowledge attacks	818
	25.3.2.1 Bandwagon Attack	818
	25.3.2.2 Segment Attack	819
	25.3.3 Nuke Attack Models	819
	25.3.3.1 Love/Hate Attack	819
	25.3.3.2 Reverse Bandwagon Attack	819
	25.3.4 Informed Attack Models	820
	25.3.4.1 Popular Attack	820
	25.3.4.2 Probe Attack Strategy	821
	25.4 Measuring Robustness	821
	25.4.1 Evaluation Metrics	822
	25.4.2 Push Attacks	823
	25.4.3 Nuke Attacks	825
	25.4.4 Informed Attacks	826
	25.4.5 Attack impact	827
	25.5 Attack Detection	827
	25.5.1 Evaluation Metrics	828
	25.5.1.1 Impact on Recommender and Attack Performance	829
	25.5.2 Single Profile Detection	829
	25.5.2.1 Unsupervised Detection	830
	25.5.2.2 Supervised Detection	830
	25.5.3 Group Profile Detection	831
	25.5.3.1 Neighbourhood Filtering	831
	25.5.3.2 Detecting attacks using Profile Clustering	832
	25.5.4 Detection findings	834
	25.6 Robust Algorithms	835
	25.6.1 Model-based Recomendation	835
	25.6.2 Robust Matrix Factorisation (RMF)	836
	25.6.3 Other Robust Recommendation Algorithms	837
	25.6.4 The Influence Limiter and Trust-based Recommendation	838
	25.7 Conclusion	839
	Acknowledgements	840
	References	840
	Index	843