book

Relevance Ranking for Vertical Search Engines

by Bo Long, Yi Chang

January 2014

Intermediate to advanced

264 pages

8h 49m

English

Morgan Kaufmann

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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

2.1 The Learning-to-Rank Approach 2.2 Joint Learning Approach from Clickthroughs 2.3 News Clustering 2.4 Summary
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
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
Introduction 5.1 Ranking Signals 5.2 Ranking Heuristics 5.3 Summary and Future Directions
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
Introduction 7.1 Related Work 7.2 Problem Formulation 7.3 Learning Aggregation Functions 7.4 Experiments 7.5 Conclusions and Future Work
Introduction 8.1 Sources of Evidence 8.2 Combination of Evidence 8.3 Evaluation 8.4 Special Topics 8.5 Conclusion
Introduction 9.1 The PCDF Model 9.2 Algorithm Derivation 9.3 Experimental Evaluation 9.4 Related Work 9.5 Conclusions

Overview

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