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 Figures

Number	Figure	Page
2.1	Scatter plot of CTR versus editor’s relevance judgments.	11
2.2	Intuitive illustration of the proposed Joint Relevance and Freshness Learning (JRFL) model. The user-issued query and the corresponding returned URLs are represented by their features on the left part. Dashed-arrow lines in the middle indicate the assumed user’s judging process before she clicks. The check boxes on the right record the clicked URLs.	13
2.3	Coordinate descent for JRFL.	15
2.4	Scatter plot of CTR versus JRFL’s prediction. (a) Object function value update. (b) Pairwise error rate (PER) update. (c) Query weight α^Q update.	20
2.5	Scatter plot of CTR versus JRFL’s prediction.	26
2.6	The architecture of the news search result clustering.