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Learning to Rank for Information Retrieval - Liu, Tie-Yan
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  • Broschiertes Buch

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, Learning to Rank for Information…mehr

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Produktbeschreibung
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, Learning to Rank for Information Retrieval 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.
Autorenporträt
Tie-Yan Liu is a lead researcher at Microsoft Research Asia. He leads a team working on learning to rank for information retrieval, and graph-based machine learning.   So far, he has more than 70 quality papers published in referred conferences and journals, including SIGIR(9), WWW(3), ICML(3), KDD, NIPS, ACM MM, IEEE TKDE, SIGKDD Explorations, etc.   He has about 40 filed US / international patents or pending applications on learning to rank, general Web search, and multimedia signal processing.   He is the co-author of the Best Student Paper for SIGIR 2008, and the Most Cited Paper for the Journal of Visual Communication and Image Representation (2004~2006). He is an Area Chair of SIGIR 2009, a Senior Program Committee member of SIGIR 2008, and Program Committee members for many other international conferences, such as WWW, ICML, ACL, and ICIP. He is the co-chair of the SIGIR workshop on learning to rank for information retrieval (LR4IR) in 2007 and 2008. He has been on the Editorial Board of the Information Retrieval Journal (IRJ) since 2008, and is the guest editor of the special issue on learning to rank of IRJ.   He has given tutorials on learning to rank at WWW 2008 and SIGIR 2008. Prior to joining Microsoft, he obtained his Ph.D. from Tsinghua University, where his research efforts were devoted to video content analysis.
Rezensionen
From the reviews:

"The book treats a very hot research topic: that of ranking great amounts of documents based on their relation to a given query, i.e., the examination of the inner mechanics of the search engines. The text is especially addressed to information retrieval and machine learning specialists and graduate students, but it might appeal to scientists from other related fields, too." (Ruxandra Stoean, Zentralblatt MATH, Vol. 1227 2012)