Relevance Ranking for Vertical Search Engines

by Bo Long, Yi Chang

Overview

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. - Foreword by Ron Brachman, Chief Scientist and Head, Yahoo! Labs - Introduces ranking algorithms and teaches readers how to manipulate ranking algorithms for the best results - Covers concepts and theories from the fundamental to the advanced - Discusses the state of the art: development of theories and practices in vertical search ranking applications - Includes detailed examples, case studies and real-world situations

Product Details

ISBN-13:	9780124072022
Publisher:	Morgan Kaufmann Publishers
Publication date:	01/25/2014
Sold by:	Barnes & Noble
Format:	eBook
Pages:	264
File size:	6 MB

About the Author

Bo Long is currently a staff applied researcher at LinkedIn Inc., and was formerly a senior research scientist at Yahoo! Labs. His research interests lie in data mining and machine learning with applications to web search, recommendation, and social network analysis. He holds eight innovations and has published peer-reviewed papers in top conferences and journals including ICML, KDD, ICDM, AAAI, SDM, CIKM, and KAIS. He has served as reviewer, workshop co-organizer, conference organizer, committee member, and area chair for multiple conferences, including KDD, NIPS, SIGIR, ICML, SDM, CIKM, JSM etc.Dr. Yi Chang is director of sciences in Yahoo Labs, where he leads the search and anti-abuse science group. His research interests include web search, applied machine learning, and social media mining. Yi has published more than 70 conference/journal papers, and he is a co-author of the book, Relevance Ranking for Vertical Search Engines. Yi is an associate editor for Neurocomputing, Pattern Recognition Letters, and he has served as workshops co-organizers, conference organizer committee members, and area chairs for multiple conferences, including WWW, SIGIR, ICML, KDD, CIKM, etc.

Table of Contents

1. Introduction2. News Search Ranking3. Medical Domain Search Ranking4. Visual Search Ranking5. Mobile Search Ranking6. Multi-Aspect Relevance Ranking7. Entity Ranking8. Aggregated Vertical Search9. Cross Vertical Search RankingReferences

What People are Saying About This

From the Publisher

Learn in-depth and systematic practices including state-of-the-art algorithms to lay a solid foundation for future advance in the field of vertical search ranking.

Editorial Reviews

"The authors of this book are active researchers in vertical search technology. This book provides researchers and application developers a comprehensive overview of the general concepts, techniques, and applications in vertical search." —Prabhakar Raghavan, Vice President of Engineering at Google

"This is an excellent book that gives the first and comprehensive introduction and overview on vertical search, an emerging and important field! Researchers and practitioners will find this book provides a comprehensive overview and systematic treatment on theories, methodologies and practices for vertical search ranking, covering several very promising topics, such as search on news and medical information, entity search, mobile search, as well as multi-aspect ranking, aggregating vertical search ranking and cross vertical ranking. I found it a great pleasure to read!" —Jiawei Han, Abel Bliss Professor, Department of Computer Science, Univ. of Illinois at Urbana-Champaign

From the Publisher