Relevance Ranking for Vertical Search Engines

역시 드럽게 어렵구만...

Relevance Ranking for Vertical Search Engines

CHAPTER 1 Introduction.............................................................. 1

1.1 Defining the Area.............................................................................1

1.2 The Content and Organization of This Book...................................1

1.3 The Audience for This Book............................................................5

1.4 Further Reading...............................................................................5

CHAPTER 2 News Search Ranking................................................ 7

2.1 The Learning-to-Rank Approach.....................................................7

2.1.1 Related Works.......................................................................8

2.1.2 Combine Relevance and Freshness......................................8

2.2 Joint Learning Approach from Clickthroughs...............................10

2.2.1 Joint Relevance and Freshness Learning............................12

2.2.2 Temporal Features..............................................................14

2.2.3 Experiment Results.............................................................17

2.2.4 Analysis of JRFL................................................................19

2.2.5 Ranking Performance.........................................................24

2.3 News Clustering.............................................................................27

2.3.1 Architecture of the System.................................................29

2.3.2 Offline Clustering...............................................................30

2.3.3 Incremental Clustering.......................................................33

2.3.4 Real-Time Clustering.........................................................34

2.3.5 Experiments........................................................................37

Summary .......................................................................................42

CHAPTER 3 Medical Domain Search Ranking.............................. 43

Introduction ...................................................................................43

3.1 Search Engines for Electronic Health Records..............................44

3.2 Search Behavior Analysis..............................................................47

3.3 Relevance Ranking........................................................................49

3.3.1 Insights from the TREC Medical Record Track.................50

3.3.2 Implementing and Evaluating Relevance

Ranking in EHR Search Engines........................................52

Contents

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3.4 Collaborative Search......................................................................54

3.5 Conclusion.....................................................................................57

CHAPTER 4 Visual Search Ranking............................................ 59

Introduction ...................................................................................59

4.1 Generic Visual Search System.......................................................60

4.2 Text-Based Search Ranking...........................................................61

4.2.1 Text Search Models............................................................61

4.2.2 Textual Query Preprocessing..............................................62

4.2.3 Text Sources.......................................................................63

4.3 Query Example-Based Search Ranking.........................................64

4.3.1 Low-Level Visual Features.................................................64

4.3.2 Distance Metrics.................................................................65

4.4 Concept-Based Search Ranking....................................................68

4.4.1 Query-Concept Mapping....................................................68

4.4.2 Search with Related Concepts............................................70

4.5 Visual Search Reranking................................................................71

4.5.1 First Paradigm: Self-Reranking..........................................71

4.5.2 Second Paradigm: Example-Based Reranking...................73

4.5.3 Third Paradigm: Crowd Reranking....................................74

4.5.4 Fourth Paradigm: Interactive Reranking.............................75

4.6 Learning and Search Ranking........................................................76

4.6.1 Ranking by Classification...................................................76

4.6.2 Classification vs. Ranking..................................................77

4.6.3 Learning to Rank................................................................78

4.7 Conclusions and Future Challenges...............................................80

CHAPTER 5 Mobile Search Ranking........................................... 81

Introduction ...................................................................................81

5.1 Ranking Signals.............................................................................83

5.1.1 Distance..............................................................................84

5.1.2 Customer Reviews and Ratings..........................................84

5.1.3 Personal Preference............................................................85

5.1.4 Search Context: Location, Time,

and Social Factors...............................................................85

5.2 Ranking Heuristics.........................................................................87

5.2.1 Dataset and Experimental Setting......................................88

5.2.2 Customer Rating.................................................................90

5.2.3 Number of Reviews............................................................95

5.2.4 Distance..............................................................................96

5.2.5 Personal Preference............................................................99

Contents vii

5.2.6 Sensitivity Analysis..........................................................102

5.3 Summary and Future Directions..................................................104

5.3.1 Evaluation of Mobile Local Search..................................104

5.3.2 User Modeling and Personalized Search..........................105

CHAPTER 6 Entity Ranking....................................................... 107

6.1 An Overview of Entity Ranking..................................................107

6.2 Background Knowledge..............................................................109

6.2.1 Terminology.....................................................................109

6.2.2 Knowledge Base...............................................................111

6.2.3 Web Search Experience....................................................112

6.3 Feature Space Analysis................................................................113

6.3.1 Probabilistic Feature Framework......................................113

6.3.2 Graph-Based Entity Popularity Feature............................115

6.4 Machine-Learned Ranking for Entities.......................................116

6.4.1 Problem Definition...........................................................117

6.4.2 Pairwise Comparison Model............................................117

6.4.3 Training Ranking Function...............................................119

6.5 Experiments.................................................................................120

6.5.1 Experimental Setup..........................................................120

6.5.2 User Data-Based Evaluation.............................................121

6.5.3 Editorial Evaluation..........................................................124

6.6 Conclusions..................................................................................125

CHAPTER 7 Multi-Aspect Relevance Ranking............................ 127

Introduction .................................................................................127

7.1 Related Work...............................................................................129

7.2 Problem Formulation...................................................................131

7.2.1 Learning to Rank for Vertical Searches............................131

7.2.2 Multi-Aspect Relevance Formulation...............................133

7.2.3 Label Aggregation............................................................133

7.2.4 Model Aggregation...........................................................134

7.3 Learning Aggregation Functions.................................................135

7.3.1 Learning Label Aggregation.............................................135

7.3.2 Learning Model Aggregation............................................137

7.4 Experiments.................................................................................138

7.4.1 Datasets.............................................................................138

7.4.2 Ranking Algorithms..........................................................140

7.4.3 Offline Experimental Results...........................................141

7.4.4 Online Experimental Results............................................143

7.5 Conclusions and Future Work......................................................145

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CHAPTER 8 Aggregated Vertical Search................................... 147

Introduction .................................................................................147

8.1 Sources of Evidence....................................................................149

8.1.1 Types of Features..............................................................149

8.1.2 Query Features..................................................................152

8.1.3 Vertical Features...............................................................153

8.1.4 Vertical-Query Features....................................................154

8.1.5 Implementation Details....................................................158

8.2 Combination of Evidence............................................................158

8.2.1 Vertical Selection..............................................................158

8.2.2 Vertical Presentation.........................................................162

8.3 Evaluation....................................................................................166

8.3.1 Vertical Selection Evaluation............................................167

8.3.2 End-to-End Evaluation.....................................................168

8.4 Special Topics..............................................................................176

8.4.1 Dealing with New Verticals..............................................176

8.4.2 Explore/Exploit.................................................................179

8.5 Conclusion...................................................................................179

CHAPTER 9 Cross-Vertical Search Ranking............................... 181

Introduction .................................................................................181

9.1 The PCDF Model.........................................................................182

9.1.1 Problem Formulation........................................................182

9.1.2 Model Formulation...........................................................183

9.2 Algorithm Derivation...................................................................186

9.2.1 Objective Specification.....................................................187

9.2.2 Optimization and Implementation....................................189

9.3 Experimental Evaluation..............................................................191

9.3.1 Data...................................................................................192

9.3.2 Experimental Setting........................................................193

9.3.3 Results and Discussions...................................................193

9.4 Related Work...............................................................................198

9.5 Conclusions..................................................................................200