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

Content preview from Relevance Ranking for Vertical Search Engines

Multi-Aspect Relevance Ranking

Abstract

In this chapter, we describe learning to rank with multi-aspect relevance for vertical searches. Many vertical searches, such as local search, focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-defined aspects. For example, in local search, text matching and distance are two important aspects to assessing relevance. Usually the overall relevance between a query and a document is a tradeoff among multiple-aspect relevancies. Given a single vertical, such a tradeoff can vary for different types of queries or in different contexts. In this chapter, we explore these vertical-specific aspects in the learning-to-rank setting. We ...