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Relevance Ranking for Vertical Search Engines

Book Description

In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications.

This reference book for professionals covers concepts and theories from the fundamental to the advanced, such as relevance, query intention, location-based relevance ranking, and cross-property ranking. It covers the most recent developments in vertical search ranking applications, such as freshness-based relevance theory for new search applications, location-based relevance theory for local search applications, and cross-property ranking theory for applications involving multiple verticals.



  • Foreword by Ron Brachman, Chief Scientist and Head, Yahoo! Labs
  • Introduces ranking algorithms and teaches readers how to manipulate ranking algorithms for the best results
  • Covers concepts and theories from the fundamental to the advanced
  • Discusses the state of the art: development of theories and practices in vertical search ranking applications
  • Includes detailed examples, case studies and real-world situations

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of Tables
  6. List of figures
  7. About the Editors
  8. List of Contributors
  9. Foreword
  10. 1: Introduction
    1. 1.1 Defining the Area
    2. 1.2 The Content and Organization of This Book
    3. 1.3 The Audience for This Book
    4. 1.4 Further Reading
  11. 2: News Search Ranking
    1. 2.1 The Learning-to-Rank Approach
    2. 2.2 Joint Learning Approach from Clickthroughs
    3. 2.3 News Clustering
    4. 2.4 Summary
  12. 3: Medical Domain Search Ranking
    1. Introduction
    2. 3.1 Search Engines for Electronic Health Records
    3. 3.2 Search Behavior Analysis
    4. 3.3 Relevance Ranking
    5. 3.4 Collaborative Search
    6. 3.5 Conclusion
  13. 4: Visual Search Ranking
    1. Introduction
    2. 4.1 Generic Visual Search System
    3. 4.2 Text-Based Search Ranking
    4. 4.3 Query Example-Based Search Ranking
    5. 4.4 Concept-Based Search Ranking
    6. 4.5 Visual Search Reranking
    7. 4.6 Learning and Search Ranking
    8. 4.7 Conclusions and Future Challenges
  14. 5: Mobile Search Ranking
    1. Introduction
    2. 5.1 Ranking Signals
    3. 5.2 Ranking Heuristics
    4. 5.3 Summary and Future Directions
  15. 6: Entity Ranking
    1. 6.1 An Overview of Entity Ranking
    2. 6.2 Background Knowledge
    3. 6.3 Feature Space Analysis
    4. 6.4 Machine-Learned Ranking for Entities
    5. 6.5 Experiments
    6. 6.6 Conclusions
  16. 7: Multi-Aspect Relevance Ranking
    1. Introduction
    2. 7.1 Related Work
    3. 7.2 Problem Formulation
    4. 7.3 Learning Aggregation Functions
    5. 7.4 Experiments
    6. 7.5 Conclusions and Future Work
  17. 8: Aggregated Vertical Search
    1. Introduction
    2. 8.1 Sources of Evidence
    3. 8.2 Combination of Evidence
    4. 8.3 Evaluation
    5. 8.4 Special Topics
    6. 8.5 Conclusion
  18. 9: Cross-Vertical Search Ranking
    1. Introduction
    2. 9.1 The PCDF Model
    3. 9.2 Algorithm Derivation
    4. 9.3 Experimental Evaluation
    5. 9.4 Related Work
    6. 9.5 Conclusions
  19. References
  20. Author Index
  21. Subject Index