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

Format:

Book

Author/Creator:

Long, Bo, author.

Contributor:

Long, Bo, editor of compilation.

Chang, Yi (Computer expert), editor of compilation.

Series:

Gale eBooks

Language:

English

Subjects (All):

Text processing (Computer science).

Sorting (Electronic computers).

Relevance.

Database searching.

Search engines--Programming.

Search engines.

Physical Description:

1 online resource (xxiii, 239 pages) : illustrations (some color)

Edition:

1st edition

Place of Publication:

Amsterdam : Morgan Kaufmann, an imprint of Elsevier, 2014.

Language Note:

English

System Details:

text file

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

Contents:

Half Title; Title Page; Copyright; Contents; List of Tables; List of Figures; About the Editors; List of Contributors; Foreword; 1 Introduction; 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 News Search Ranking; 2.1 The Learning-to-Rank Approach; 2.1.1 Related Works; 2.1.2 Combine Relevance and Freshness; 2.1.2.1 Training Sample Collection; 2.1.2.2 Editorial Labeling; 2.2 Joint Learning Approach from Clickthroughs; 2.2.1 Joint Relevance and Freshness Learning; 2.2.2 Temporal Features; 2.2.2.1 URL Freshness Features

2.2.2.2 Query Freshness Features2.2.3 Experiment Results; 2.2.3.1 Datasets; 2.2.3.2 Click Datasets; 2.2.3.3 Preference Pair Selection; 2.2.3.4 Temporal Feature Implementation; 2.2.3.5 Baselines and Evaluation Metrics; 2.2.4 Analysis of JRFL; 2.2.4.1 Convergency; 2.2.4.2 Relevance and Freshness Learning; 2.2.4.3 Query Weight Analysis; 2.2.5 Ranking Performance; 2.3 News Clustering; 2.3.1 Architecture of the System; 2.3.2 Offline Clustering; 2.3.2.1 Feature Vector Generation; 2.3.2.2 Minhash Signature Generation; 2.3.2.3 Duplicate Detection; 2.3.2.4 Locality-Sensitive Hashing

2.3.2.5 Correlation Clustering2.3.2.6 Evaluation; 2.3.3 Incremental Clustering; 2.3.4 Real-Time Clustering; 2.3.4.1 Meta Clustering and Textual Matching; 2.3.4.2 Contextual Query-Based Term Weighting; 2.3.4.3 Offline Clusters as Features; 2.3.4.4 Performance Analysis; 2.3.5 Experiments; 2.3.5.1 Experimental Setup; 2.3.5.2 Evaluation Metrics; 2.3.5.3 Evaluating Meta Clustering and Textual Matching; 2.3.5.4 Results with QrySim; 2.3.5.5 Results with Offline Clusters as Features; 3 Medical Domain Search Ranking; 3.1 Search Engines for Electronic Health Records; 3.2 Search Behavior Analysis

3.3 Relevance Ranking3.3.1 Insights from the TREC Medical Record Track; 3.3.2 Implementing and Evaluating Relevance Ranking in EHR Search Engines; 3.4 Collaborative Search; 3.5 Conclusion; 4 Visual Search Ranking; 4.1 Generic Visual Search System; 4.2 Text-Based Search Ranking; 4.2.1 Text Search Models; 4.2.2 Textual Query Preprocessing; 4.2.2.1 Query Expansion; 4.2.2.2 Stemming Algorithm; 4.2.2.3 Stopword Removal; 4.2.2.4 N-Gram Query Segmentation; 4.2.2.5 Part-of-Speech Tagging; 4.2.3 Text Sources; 4.3 Query Example-Based Search Ranking; 4.3.1 Low-Level Visual Features

4.3.1.1 Global Feature4.3.1.2 Region Features; 4.3.1.3 Local Features; 4.3.2 Distance Metrics; 4.4 Concept-Based Search Ranking; 4.4.1 Query-Concept Mapping; 4.4.2 Search with Related Concepts; 4.5 Visual Search Reranking; 4.5.1 First Paradigm: Self-Reranking; 4.5.2 Second Paradigm: Example-Based Reranking; 4.5.3 Third Paradigm: Crowd Reranking; 4.5.4 Fourth Paradigm: Interactive Reranking; 4.6 Learning and Search Ranking; 4.6.1 Ranking by Classification; 4.6.2 Classification vs. Ranking; 4.6.3 Learning to Rank; 4.7 Conclusions and Future Challenges; 5 Mobile Search Ranking

5.1 Ranking Signals

Notes:

Description based upon print version of record.

Includes bibliographical references and indexes.

Description based on print version record.

ISBN:

9780124072022

012407202X

OCLC:

870333355