Social Information Access Systems and Technologies [1st ed. 2018] 9783319900919, 9783319900926, 3319900919, 3319900927

Table of contents :
Preface......Page 6
Contents......Page 7
1 Social Information Access......Page 9
2 The Emergence of Social Information Access......Page 10
3.1 Types of Information Access......Page 12
3.2 Making Information Access Social......Page 14
3.3 Types and Sources of Social Information......Page 16
4 The Book Structure......Page 20
References......Page 23
1 Introduction......Page 27
1.1 The Recipient......Page 28
1.2 The Personal Information......Page 29
1.3 The Use of the Information......Page 30
1.4 The Decision......Page 32
1.6 The Problems......Page 33
1.7 Outline of the Chapter......Page 34
2 Disclosure to a System: Aggregators......Page 35
2.2 Examples......Page 36
2.4 Technical Solutions......Page 38
2.5 Decision-Support Solutions......Page 39
3.1 Motivations......Page 41
3.2 Examples......Page 42
3.4 Technical Solutions......Page 44
4 Disclosure to Known Contacts: Social Network-Based Systems......Page 45
4.1 Motivations......Page 46
4.2 Examples......Page 47
4.3 Individual Differences Between Users......Page 49
4.4 Technical Solutions......Page 51
4.5 Decision-Support Solutions......Page 52
5 SIA in Support of Privacy......Page 54
5.1 Explicit Use of Social Information in Privacy Decision Support......Page 55
5.2 Implicit Use of Social Information in Privacy Decision Support......Page 56
6 Recommendations for Future Work......Page 58
7 Conclusion......Page 60
References......Page 61
1 Introduction......Page 83
2 Common Features......Page 84
3 Research Trends......Page 86
3.1 User Motivations......Page 88
3.2 Answer Quality and Credibility Judgment......Page 90
3.3 Domain-Specific Studies......Page 91
4.1 Social Theories......Page 93
5 Research Methods and Techniques......Page 94
5.1 Data Collection Using Application Program Interfaces (APIs)......Page 95
5.2 Social Network Analysis......Page 97
5.3 Content Analysis......Page 99
5.4 Text Mining......Page 104
5.5 Interviews and Surveys......Page 107
References......Page 109
1 Introduction......Page 116
2.1 Definition of Collaboration......Page 117
2.2 Collaboration and Teamwork Processes......Page 118
2.4 Factors Affecting Collaboration......Page 119
3 Collaboration in Information Search......Page 120
3.1 Collaborative Information Search in the Web Environment......Page 121
3.2 Collaborative Information Search in Academic Settings......Page 122
3.3 Collaborative Information Search in Other Settings......Page 123
3.4 Collaborative Information Search Processes......Page 124
4 Factors Affecting Collaborative Information Search......Page 126
4.1 People-Side Factors......Page 127
4.2 Search-Side Factors......Page 131
5.1 Interface-Level Support......Page 134
5.2 Algorithm-Level Support......Page 138
6.1 System-Oriented Evaluation......Page 142
7.1 Discussion......Page 143
References......Page 145
1 Introduction......Page 150
2 Supporting Theories......Page 151
3.1 Guidance......Page 153
3.3 Engagement......Page 154
4 Pioneer Examples of Social Navigation......Page 155
5.1 Users' Activities and Their Traces......Page 157
5.2 History Enriched Environments via Implicit Asynchronous Traces......Page 158
5.3 Co-presence Enriched Environments via Implicit Synchronous Traces......Page 160
5.4 Sharing Destinations and Paths via Explicit Asynchronous Traces......Page 161
6 Addressing the Challenges of Social Navigation Support......Page 164
6.1 AnnotatEd and KALAS: Exploring More Reliable Traces......Page 167
6.2 Conference Navigator: Reliable Privacy-Protected Traces......Page 168
6.3 Comtella and CourseAgent: Engaging Users......Page 169
6.4 Progressor: Social Navigation and Engagement with Social Comparison......Page 171
7 Social Navigation Beyond Hypertext and Hyperlinks......Page 172
7.1 Spatial Social Navigation......Page 173
7.2 Social Navigation in Continuous Media......Page 174
7.3 Integrating Social Navigation with Other Social Information Access Approaches......Page 176
8.1 Overall Impact of Social Navigation on Users' Behavior......Page 178
8.3 Circumstances Under Which Social Navigation Support is Effective......Page 179
9 Concluding Remarks......Page 180
References......Page 181
1 Introduction......Page 189
2 Social Tagging......Page 191
3 User Interfaces and Visualization......Page 192
3.1 Tag Clouds......Page 193
3.2 Integrated Interfaces......Page 201
4 Tag Clustering......Page 202
4.1 Flat Tag Clustering......Page 203
4.2 Hierarchical Tag Clustering......Page 204
5 Modeling Navigation in Social Tagging Systems......Page 207
5.2 Decentralized Search......Page 208
6.1 Network Theoretic Perspective......Page 210
6.3 Information Foraging Perspective......Page 213
6.4 Tagging vs. Library Approach......Page 214
References......Page 215
1 Introduction......Page 221
2.1 Defining Social Search......Page 223
2.2 Sources of Social Information......Page 224
2.3 The Social Search Process......Page 226
2.4 The Big Picture View......Page 228
3 Query Formulation, Elaboration, and Recommendation......Page 229
4 Query Expansion......Page 230
5 Social Indexing and Matching......Page 233
5.2 Query Logs......Page 235
5.3 Annotations and Tags......Page 236
5.4 Blogs and Microblogs......Page 237
6 Ranking......Page 238
6.1 Web Links for Primary Ranking......Page 239
6.2 Query Logs for Primary Ranking......Page 240
6.3 Using Browsing Trails and Page Behavior for Primary Ranking......Page 243
6.4 Reranking and Recommending Web Search Results......Page 245
6.5 Ranking and Re-ranking with Social Media Data......Page 247
7 Resource Presentation – Social Summarization......Page 249
8 Augmenting Search Results: Annotations and Explanations......Page 250
8.1 Query Logs......Page 251
8.2 Browsing and Annotation......Page 252
8.3 Social Media and Social Links......Page 253
9 Beyond the SERP......Page 255
10.1 Antworld......Page 257
10.2 I-SPY......Page 259
10.3 HeyStaks......Page 261
10.4 Social Search Engine – Search with Social Links......Page 264
11 Expanding the Borders of Social Search......Page 266
11.1 ASSIST – From Social Search to Social Navigation......Page 267
11.2 Aardvark – From Social Search to Social Q&A......Page 269
12 Conclusions......Page 270
References......Page 271
1 Introduction......Page 285
2.2 Social Search......Page 287
2.3 Applying Networks for Social Search......Page 288
3.1 Measuring Node Similarity in Networks......Page 292
3.2 Modular Structure in Networks......Page 293
3.3 Measuring Node Importance in Networks......Page 295
4.1 Applying People-Centric Network for Document Search......Page 297
4.2 Applying Document-Centric Networks for Document Search......Page 299
4.3 Applying Heterogeneous Networks for Document Search......Page 301
5.1 People Search......Page 302
5.2 Applying People-Centric Networks for People Search......Page 304
5.4 Applying Heterogeneous Networks for People Search......Page 306
6.2 Challenges......Page 307
References......Page 308
9Accessing Information with Tags: Search and Ranking......Page 318
1 Introduction......Page 319
2 Tagging Systems......Page 320
2.1 Example Systems......Page 322
2.2 Design and Functionality of Tagging Systems......Page 324
2.3 Strengths and Weaknesses of Tagging Systems......Page 325
2.5 Classification of Tags......Page 326
2.6 Types of Annotators......Page 327
3.1 Formal Model for Tagging......Page 328
3.2 Network Properties of Folksonomies......Page 330
4 Ranking in Folksonomies......Page 333
4.1 The FolkRank Algorithm......Page 334
4.2 Adjusted Versions of the HITS Algorithm to a Folksonomy......Page 335
5 Web Search with Folksonomies......Page 336
5.1 Comparing Traditional and Tag-Based Search......Page 337
5.2 Integration of Tags in Search......Page 338
5.3 Advanced Integration of Tags in Search......Page 340
6 Logsonomies: A Unified View on Search and Tagging......Page 342
7 Future of Search and Ranking in Tagging......Page 343
References......Page 344
1 Introduction......Page 352
1.1 Examples of Recommender Systems......Page 354
1.2 A Note on the Organization of Recommendation Algorithms......Page 357
2 Concepts and Notation......Page 359
3 Baseline Predictors......Page 361
4.1 User-User......Page 363
4.2 Item-Item......Page 366
5 Matrix Factorization Algorithms......Page 368
5.1 Training Matrix Decomposition Models With Singular Value Decomposition......Page 369
5.2 Training Matrix Decomposition Models With Gradient Descent......Page 370
6 Learning to Rank......Page 372
6.1 BPR......Page 373
7.2 Linear Regression Approaches......Page 375
8 Combining Algorithms......Page 376
8.1 Ensemble Recommendation......Page 377
8.2 Recommending for Novelty and Diversity......Page 379
9 Metrics and Evaluation......Page 380
9.1 Prediction Metrics......Page 381
9.2 Ranking Quality......Page 383
9.3 Decision Support Metrics......Page 384
9.4 Novelty and Diversity......Page 387
9.5 Structuring an Offline Evaluation......Page 388
9.6 Online Evaluations......Page 389
9.7 Resources for Algorithm Evaluation......Page 392
References......Page 393
1 Introduction......Page 399
2 A Range of Definitions of Social Recommendations......Page 400
3.1 Challenges of Traditional Collaborative Filtering Recommendations......Page 402
3.2 Online Social Networks: A Useful Source of Information......Page 403
4 Multiple Dimensions of Social Link-Based Recommendations......Page 405
4.1 Input Data Types of Social Link-Based Recommendations......Page 410
4.2 Applications and Target Items of Social Link-Based Recommendations......Page 411
4.3 Types of Social Connections Employed in Link-Based Recommendations......Page 414
5 Algorithms for Recommendations Based on Social Links......Page 416
5.1 Direct Friend-to-Friend Recommendations......Page 423
5.2 Nearest Neighbor-Based Recommendation Approach......Page 425
5.3 Recommendation Algorithms Based on Matrix Factorization......Page 428
5.5 Advanced Hybrid Recommendation Approaches......Page 432
6.1 Evaluation of Social Recommendation......Page 434
6.2 Explanations of Recommendations Based on Social Links......Page 436
6.3 Cross-System and Multidimensional Online Social Networks......Page 437
6.4 Privacy in Online Social Networks......Page 439
References......Page 440
1 Introduction......Page 449
2 Preliminaries......Page 450
3 Item Recommendation......Page 454
4.1 Nearest-Neighbor Algorithms......Page 455
4.2 Dimensionality Reduction......Page 459
4.3 Graph-Based Recommendation......Page 463
5 Content-Based Filtering......Page 466
6 Machine Learning......Page 470
7 Hybrid Recommendation......Page 471
8 Tag Recommendation......Page 474
9 Conclusions......Page 475
References......Page 478
1 Introduction......Page 488
2.1 Collaborative Filtering......Page 489
2.2 Content-Based and Hybrid Recommendation......Page 490
2.3 User-Generated Content for Recommendation......Page 491
2.4 Review Filtering, Quality and Spam......Page 493
3.1 Review-Based Recommendation Approach......Page 494
3.2 Evaluation......Page 496
4 Case Study 2 – Opinionated Recommendation......Page 501
4.2 Identifying Review Features......Page 502
4.4 From Review Features to Item Descriptions......Page 503
4.5 Recommending Products......Page 504
4.6 Evaluation......Page 505
5 Case Study 3 – Review Helpfulness Classification......Page 507
5.1 Classifying Review Helpfulness......Page 508
5.2 Evaluation......Page 509
6 Conclusions......Page 511
References......Page 512
1 Introduction and Motivation......Page 518
2 Explicit vs. Implicit Feedback......Page 521
2.1 Explicit Feedback......Page 522
2.2 Implicit Feedback......Page 523
2.3 Challenges of Using Implicit Feedback......Page 525
3.1 Types of Observed Behavior in Applications......Page 526
3.2 An Extended Categorization of Observable User Behavior......Page 532
4.1 Converting Implicit Signals to Ratings......Page 533
4.2 One-Class Collaborative Filtering Techniques......Page 537
4.3 Frequent Patterns in Implicit Feedback......Page 544
4.4 Hybrid Implicit-Explicit Techniques......Page 547
5.1 Recommendation as a Classification and Ranking Task......Page 551
5.2 Domain-Dependent Evaluation Approaches......Page 553
5.3 Discussion......Page 554
6.1 Recommending Based on Activity Logs in E-Commerce......Page 556
6.2 Next-Track Music Recommendations from Shared Playlists......Page 561
6.3 Considering Application-Specific Requirements in BPR......Page 564
7 Summary and Outlook......Page 568
References......Page 569
1 Introduction......Page 578
2.1 Fundamental Techniques......Page 579
2.2 Network Types......Page 580
2.3 Relationship Types......Page 582
3.1 ``Regular'' Networks......Page 583
3.2 Enterprise Case Studies......Page 585
3.3 Different Friend Recommendation Approaches......Page 594
3.4 Ad-Hoc Networks......Page 595
4 Recommending Interesting People......Page 596
4.1 Followee Recommendation......Page 597
4.2 Ad-Hoc Networks......Page 602
5.1 Enterprise Case Studies......Page 604
5.2 Refined Approaches......Page 609
5.3 Ad-Hoc Networks......Page 610
5.4 Dating Recommendations......Page 611
6.1 Link Prediction......Page 612
6.2 People Search and Expertise Location......Page 613
7.1 Key Topics......Page 615
7.2 Directions for Future Work......Page 619
References......Page 622
1 Introduction......Page 632
2.2 Venue Data......Page 634
2.3 Smartphones and Urban Sensors......Page 635
3 Recommending Using Location Data......Page 636
3.1 Recommending New Places......Page 637
3.2 Recommending the Next Place......Page 642
3.3 Recommending Events and Neighborhoods......Page 648
3.4 Personalised Place Search......Page 651
4 Evaluating Location Recommendations......Page 655
5 Conclusion......Page 656
References......Page 657
Author Index......Page 662