Relevance Ranking for Vertical Search Engines

Relevance Ranking for Vertical Search Engines

1st Edition - January 25, 2014
  • Authors: Bo Long, Yi Chang
  • Paperback ISBN: 9780124071711
  • eBook ISBN: 9780124072022

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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.

Key Features

  • 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


Software Engineers, Computer Scientists, Academic Researchers, Applied Scientists Web professionals and Researchers.

Table of Contents

    • 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

Product details

  • No. of pages: 264
  • Language: English
  • Copyright: © Morgan Kaufmann 2014
  • Published: January 25, 2014
  • Imprint: Morgan Kaufmann
  • Paperback ISBN: 9780124071711
  • eBook ISBN: 9780124072022

About the Authors

Bo Long

Bo Long
Bo Long is currently a staff applied researcher at LinkedIn Inc., and was formerly a senior research scientist at Yahoo! Labs. His research interests lie in data mining and machine learning with applications to web search, recommendation, and social network analysis. He holds eight innovations and has published peer-reviewed papers in top conferences and journals including ICML, KDD, ICDM, AAAI, SDM, CIKM, and KAIS. He has served as reviewer, workshop co-organizer, conference organizer, committee member, and area chair for multiple conferences, including KDD, NIPS, SIGIR, ICML, SDM, CIKM, JSM etc.

Affiliations and Expertise

Staff applied researcher at LinkedIn Inc.

Yi Chang

Yi Chang
Dr. Yi Chang is director of sciences in Yahoo Labs, where he leads the search and anti-abuse science group. His research interests include web search, applied machine learning, and social media mining. Yi has published more than 70 conference/journal papers, and he is a co-author of the book, Relevance Ranking for Vertical Search Engines. Yi is an associate editor for Neurocomputing, Pattern Recognition Letters, and he has served as workshops co-organizers, conference organizer committee members, and area chairs for multiple conferences, including WWW, SIGIR, ICML, KDD, CIKM, etc.

Affiliations and Expertise

Director of Sciences at Yahoo Labs, Sunnyvale, CA, USA