Title Relevance ranking for vertical search engines / edited by Bo Long, Yi Chang

Published Amsterdam : Elsevier/Morgan Kaufmann, [2014] ©2014

Table of Contents
	List of Tables					ix
	List of Figures					xiii
	About the Editors					xvii
	Contributors					xix
	Foreword					xxi
ch. 1	Introduction					1
1.1.	Defining the Area					1
1.2.	The Content and Organization of This Book					1
1.3.	The Audience for This Book					5
1.4.	Further Reading					5
ch. 2	News Search Ranking					7
2.1.	The Learning-to-Rank Approach					7
2.1.1.	Related Works					8
2.1.2.	Combine Relevance and Freshness					8
2.2.	Joint Learning Approach from Clickthroughs					10
2.2.1.	Joint Relevance and Freshness Learning					12
2.2.2.	Temporal Features					14
2.2.3.	Experiment Results					17
2.2.4.	Analysis of JRFL					19
2.2.5.	Ranking Performance					24
2.3.	News Clustering					27
2.3.1.	Architecture of the System					29
2.3.2.	Offline Clustering					30
2.3.3.	Incremental Clustering					33
2.3.4.	Real-Time Clustering					34
2.3.5.	Experiments					37
	Summary					42
ch. 3	Medical Domain Search Ranking					43
	Introduction					43
3.1.	Search Engines for Electronic Health Records					44
3.2.	Search Behavior Analysis					47
3.3.	Relevance Ranking					49
3.3.1.	Insights from the TREC Medical Record Track					50
3.3.2.	Implementing and Evaluating Relevance Ranking in EHR Search Engines					52
3.4.	Collaborative Search					54
3.5.	Conclusion					57
ch. 4	Visual Search Ranking					59
	Introduction					59
4.1.	Generic Visual Search System					60
4.2.	Text-Based Search Ranking					61
4.2.1.	Text Search Models					61
4.2.2.	Textual Query Preprocessing					62
4.2.3.	Text Sources					63
4.3.	Query Example-Based Search Ranking					64
4.3.1.	Low-Level Visual Features					64
4.3.2.	Distance Metrics					65
4.4.	Concept-Based Search Ranking					68
4.4.1.	Query-Concept Mapping					68
4.4.2.	Search with Related Concepts					70
4.5.	Visual Search Reranking					71
4.5.1.	First Paradigm: Self-Reranking					71
4.5.2.	Second Paradigm: Example-Based Reranking					73
4.5.3.	Third Paradigm: Crowd Reranking					74
4.5.4.	Fourth Paradigm: Interactive Reranking					75
4.6.	Learning and Search Ranking					76
4.6.1.	Ranking by Classification					76
4.6.2.	Classification vs. Ranking					77
4.6.3.	Learning to Rank					78
4.7.	Conclusions and Future Challenges					80
ch. 5	Mobile Search Ranking					81
	Introduction					81
5.1.	Ranking Signals					83
5.1.1.	Distance					84
5.1.2.	Customer Reviews and Ratings					84
5.1.3.	Personal Preference					85
5.1.4.	Search Context: Location, Time, and Social Factors					85
5.2.	Ranking Heuristics					87
5.2.1.	Dataset and Experimental Setting					88
5.2.2.	Customer Rating					90
5.2.3.	Number of Reviews					95
5.2.4.	Distance					96
5.2.5.	Personal Preference					99
5.2.6.	Sensitivity Analysis					102
5.3.	Summary and Future Directions					104
5.3.1.	Evaluation of Mobile Local Search					104
5.3.2.	User Modeling and Personalized Search					105
ch. 6	Entity Ranking					107
6.1.	An Overview of Entity Ranking					107
6.2.	Background Knowledge					109
6.2.1.	Terminology					109
6.2.2.	Knowledge Base					111
6.2.3.	Web Search Experience					112
6.3.	Feature Space Analysis					113
6.3.1.	Probabilistic Feature Framework					113
6.3.2.	Graph-Based Entity Popularity Feature					115
6.4.	Machine-Learned Ranking for Entities					116
6.4.1.	Problem Definition					117
6.4.2.	Pairwise Comparison Model					117
6.4.3.	Training Ranking Function					119
6.5.	Experiments					120
6.5.1.	Experimental Setup					120
6.5.2.	User Data-Based Evaluation					121
6.5.3.	Editorial Evaluation					124
6.6.	Conclusions					125
ch. 7	Multi-Aspect Relevance Ranking					127
	Introduction					127
7.1.	Related Work					129
7.2.	Problem Formulation					131
7.2.1.	Learning to Rank for Vertical Searches					131
7.2.2.	Multi-Aspect Relevance Formulation					133
7.2.3.	Label Aggregation					133
7.2.4.	Model Aggregation					134
7.3.	Learning Aggregation Functions					135
7.3.1.	Learning Label Aggregation					135
7.3.2.	Learning Model Aggregation					137
7.4.	Experiments					138
7.4.1.	Datasets					138
7.4.2.	Ranking Algorithms					140
7.4.3.	Offline Experimental Results					141
7.4.4.	Online Experimental Results					143
7.5.	Conclusions and Future Work					145
ch. 8	Aggregated Vertical Search					147
	Introduction					147
8.1.	Sources of Evidence					149
8.1.1.	Types of Features					149
8.1.2.	Query Features					152
8.1.3.	Vertical Features					153
8.1.4.	Vertical-Query Features					154
8.1.5.	Implementation Details					158
8.2.	Combination of Evidence					158
8.2.1.	Vertical Selection					158
8.2.2.	Vertical Presentation					162
8.3.	Evaluation					166
8.3.1.	Vertical Selection Evaluation					167
8.3.2.	End-to-End Evaluation					168
8.4.	Special Topics					176
8.4.1.	Dealing with New Verticals					176
8.4.2.	Explore/Exploit					179
8.5.	Conclusion					179
ch. 9	Cross-Vertical Search Ranking					181
	Introduction					181
9.1.	The PCDF Model					182
9.1.1.	Problem Formulation					182
9.1.2.	Model Formulation					183
9.2.	Algorithm Derivation					186
9.2.1.	Objective Specification					187
9.2.2.	Optimization and Implementation					189
9.3.	Experimental Evaluation					191
9.3.1.	Data					192
9.3.2.	Experimental Setting					193
9.3.3.	Results and Discussions					193
9.4.	Related Work					198
9.5.	Conclusions					200
	References					201
	Author Index					223
	Subject Index					233

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Description 1 online resource (xxiii, 239 pages) : illustrations (some color) Contents News search ranking -- Medical domain search ranking -- Visual search ranking -- Mobile search ranking -- Entity ranking -- Multi-aspect relevance ranking -- Aggregated vertical search -- Cross vertical search ranking Summary 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. Introduces ranking algorithms and teaches readers how to manipulate ranking algorithms for the best resultsCovers concepts and theories from the fundamental to the advancedDiscusses the state of the art: development of theories and practices in vertical search ranking applicationsIncludes detailed examples, case studies and real-world examples Bibliography Includes bibliographical references (pages 201-221) and index Notes Print version record Subject Text processing (Computer science) Sorting (Electronic computers) Relevance. Database searching. Search engines -- Programming. Word Processing online searching. LANGUAGE ARTS & DISCIPLINES -- Library & Information Science -- General. Database searching Relevance Search engines -- Programming Sorting (Electronic computers) Text processing (Computer science) Form Electronic book Author Long, Bo, editor Chang, Yi (Writer on computers), editor ISBN 9780124072022 012407202X 9781306415439 1306415438

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