book

Relevance Ranking for Vertical Search Engines

January 2014

Intermediate to advanced

264 pages

8h 49m

English

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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

List of Tables

Number	Table	Page
2.1	Temporal features for URL freshness and query model.	16
2.2	Evaluation corpus.	19
2.3	Feature weights learned by JRFL.	21
2.4	Performance of individual relevance and freshness estimations.	22
2.5	Query intention analysis by the inferred query weight.	23
2.6	Query length distribution under different query categories.	23
2.7	Comparison of random bucket clicks.	24
2.8	Comparison of normal clicks.	24
2.9	Comparison of editorial annotations.	25
2.10	Case study: Degenerated ranking results by JRFL for query “afghanistan.”	25
2.11	Performance of offline clustering system.	33
2.12	Results with various simple offline clustering algorithms and the real-time clustering algorithm, which includes the metaclustering algorithm.	40
2.13	Real-time ...