
Relevance Ranking for Vertical Search Engines
- 1st Edition - January 25, 2014
- Imprint: Morgan Kaufmann
- Authors: Bo Long, Yi Chang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 4 0 7 1 7 1 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 7 2 0 2 - 2
In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Searc… Read more

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Request a sales quoteIn 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
Software Engineers, Computer Scientists, Academic Researchers, Applied Scientists Web professionals and Researchers.
- 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
- Edition: 1
- Published: January 25, 2014
- Imprint: Morgan Kaufmann
- No. of pages: 264
- Language: English
- Paperback ISBN: 9780124071711
- eBook ISBN: 9780124072022
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Bo Long
YC