Relevance Ranking for Vertical Search Engines by Long, Bo -- Read -- Imperial Library of Trantor

Index

Cover image Title page Table of Contents Copyright List of Tables List of figures About the Editors List of Contributors Foreword 1: Introduction

1.1 Defining the Area 1.2 The Content and Organization of This Book 1.3 The Audience for This Book 1.4 Further Reading

2: News Search Ranking

2.1 The Learning-to-Rank Approach 2.2 Joint Learning Approach from Clickthroughs 2.3 News Clustering 2.4 Summary

3: Medical Domain Search Ranking

Introduction 3.1 Search Engines for Electronic Health Records 3.2 Search Behavior Analysis 3.3 Relevance Ranking 3.4 Collaborative Search 3.5 Conclusion

4: Visual Search Ranking

Introduction 4.1 Generic Visual Search System 4.2 Text-Based Search Ranking 4.3 Query Example-Based Search Ranking 4.4 Concept-Based Search Ranking 4.5 Visual Search Reranking 4.6 Learning and Search Ranking 4.7 Conclusions and Future Challenges

5: Mobile Search Ranking

Introduction 5.1 Ranking Signals 5.2 Ranking Heuristics 5.3 Summary and Future Directions

6: Entity Ranking

6.1 An Overview of Entity Ranking 6.2 Background Knowledge 6.3 Feature Space Analysis 6.4 Machine-Learned Ranking for Entities 6.5 Experiments 6.6 Conclusions

7: Multi-Aspect Relevance Ranking

Introduction 7.1 Related Work 7.2 Problem Formulation 7.3 Learning Aggregation Functions 7.4 Experiments 7.5 Conclusions and Future Work

8: Aggregated Vertical Search

Introduction 8.1 Sources of Evidence 8.2 Combination of Evidence 8.3 Evaluation 8.4 Special Topics 8.5 Conclusion

9: Cross-Vertical Search Ranking

Introduction 9.1 The PCDF Model 9.2 Algorithm Derivation 9.3 Experimental Evaluation 9.4 Related Work 9.5 Conclusions

References Author Index Subject Index