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

Mobile Search Ranking

Abstract

The wide availability of Internet access on mobile devices, such as phones and personal media players, has allowed users to search and access Web information on the go. The availability of continuous fine-grained location information on these devices has enabled mobile local search, which employs user location as a key factor to search for local entities (e.g., a restaurant, store, gas station, or attraction), to overtake a significant part of the query volume. This is also evident by the rising popularity of location-based search engines on mobile devices, such as Bing Local, Google Local, Yahoo! Local, and Yelp. The quality of any mobile local search engine is mainly determined by its ranking function, which ...