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Learning to Rank for Information Retrieval (Hardcover, 2011 Ed.): Tie-Yan Liu Learning to Rank for Information Retrieval (Hardcover, 2011 Ed.)
Tie-Yan Liu
R3,629 Discovery Miles 36 290 Ships in 12 - 17 working days

Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called "learning to rank." Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches - these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.

Information Retrieval - 24th China Conference, CCIR 2018, Guilin, China, September 27-29, 2018, Proceedings (Paperback, 1st ed.... Information Retrieval - 24th China Conference, CCIR 2018, Guilin, China, September 27-29, 2018, Proceedings (Paperback, 1st ed. 2018)
Shichao Zhang, Tie-Yan Liu, Xianxian Li, Jiafeng Guo, Chenliang Li
R1,472 Discovery Miles 14 720 Ships in 10 - 15 working days

This book constitutes the refereed proceedings of the 24th China Conference on Information Retrieval, CCIR 2018, held in Guilin, China, in September 2018. The 22 full papers presented were carefully reviewed and selected from 52 submissions. The papers are organized in topical sections: Information retrieval, collaborative and social computing, natural language processing.

Web and Internet Economics - 10th International Conference, WINE 2014, Beijing, China, December 14-17, 2014, Proceedings... Web and Internet Economics - 10th International Conference, WINE 2014, Beijing, China, December 14-17, 2014, Proceedings (Paperback, 2014 ed.)
Tie-Yan Liu, Qi Qi, Yinyu Ye
R2,898 Discovery Miles 28 980 Ships in 10 - 15 working days

This book constitutes the thoroughly refereed conference proceedings of the 10th International Conference on Web and Internet Economics, WINE 2014, held in Beijing, China, in December 2014. The 32 regular and 13 short papers were carefully reviewed and selected from 107 submissions and cover results on incentives and computation in theoretical computer science, artificial intelligence, and microeconomics.

Learning to Rank for Information Retrieval (Paperback, 2011 ed.): Tie-Yan Liu Learning to Rank for Information Retrieval (Paperback, 2011 ed.)
Tie-Yan Liu
R3,991 Discovery Miles 39 910 Ships in 10 - 15 working days

Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called "learning to rank". Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches - these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.

Information Retrieval Technology - 9th Asia Information Retrieval Societies Conference, AIRS 2013, Singapore, December 9-11,... Information Retrieval Technology - 9th Asia Information Retrieval Societies Conference, AIRS 2013, Singapore, December 9-11, 2013, Proceedings (Paperback, 2013)
Rafael Banchs, Fabrizio Silvestri, Tie-Yan Liu, Min Zhang, Sheng Gao, …
R3,036 Discovery Miles 30 360 Ships in 10 - 15 working days

This book constitutes the refereed proceedings of the 9th Information Retrieval Societies Conference, AIRS 2013, held in Singapore, in December 2013. The 27 full papers and 18 poster presentations included in this volume were carefully reviewed and selected from 109 submissions. They are organized in the following topical sections: IR theory, modeling and query processing; clustering, classification and detection; natural language processing for IR; social networks, user-centered studies and personalization and applications.

Social Informatics - 7th International Conference, SocInfo 2015, Beijing, China, December 9-12, 2015, Proceedings (Paperback,... Social Informatics - 7th International Conference, SocInfo 2015, Beijing, China, December 9-12, 2015, Proceedings (Paperback, 1st ed. 2015)
Tie-Yan Liu, Christie Napa Scollon, Wenwu Zhu
R2,290 Discovery Miles 22 900 Ships in 10 - 15 working days

This book constitutes the proceedings of the 7th International Conference on Social Informatics, SocInfo 2015, held in Beijing, China, in December 2015. The 19 papers presented in this volume were carefully reviewed and selected from 42 submissions. They cover topics such as user modeling, opinion mining, user behavior, and crowd sourcing.

Learning to Rank for Information Retrieval (Paperback): Tie-Yan Liu Learning to Rank for Information Retrieval (Paperback)
Tie-Yan Liu
R1,909 Discovery Miles 19 090 Ships in 10 - 15 working days

Learning to Rank for Information Retrieval is an introduction to the field of learning to rank, a hot research topic in information retrieval and machine learning. It categorizes the state-of-the-art learning-to-rank algorithms into three approaches from a unified machine learning perspective, describes the loss functions and learning mechanisms in different approaches, reveals their relationships and differences, shows their empirical performances on real IR applications, and discusses their theoretical properties such as generalization ability. As a tutorial, this bookl helps people find the answers to the following critical questions: To what respect are learning-to-rank algorithms similar and in which aspects do they differ? What are the strengths and weaknesses of each algorithm? Which learning-to-rank algorithm empirically performs the best? Is ranking a new machine learning problem? What are the unique theoretical issues for ranking as compared to classification and regression? Learning to Rank for Information Retrieval is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners.

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