Relevance Ranking for Vertical Search Engines

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9780124071711: Relevance Ranking for Vertical Search Engines

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.

For interactive learning, the book has a companion website that provides all the updated and additional information related to the book for readers to browse and download, with content lists, sample chapters, data sets, errata, and slides.

  • 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 examples

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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.

Yi Chang is a principal scientist and sciences director in Yahoo Labs, where he leads the search and anti-abuse science group. His research interests include web search, applied machine learning, natural language processing and social computing. Yi has published more than 60 conference/journal papers, and has served as workshop co-organizer, conference organizer, committee member, and area chair for multiple conferences, including WWW, SIGIR, ICML, KDD, CIKM, etc.

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Long, Bo; Chang, Yi
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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2014. Paperback. Book Condition: New. 232 x 188 mm. Language: English . Brand New Book. 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. Bookseller Inventory # AA59780124071711

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2014. Paperback. Book Condition: New. 232 x 188 mm. Language: English . Brand New Book. 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. Bookseller Inventory # AA59780124071711

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