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Tie-Yan Liu, Microsoft Research Asia, tyliu@microsoft.com. This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval

Learning to Rank for Information Retrieval

SIGIR, no. 3 (2011): 225-331

Cited by: 3065|Views264
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Abstract

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by usin...More

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Introduction
  • Bio: Tie-Yan Liu is a lead researcher at Microsoft Research Asia. He leads a team working on learning to rank for information retrieval, and large-scale graph learning.
  • Tie-Yan Liu, Microsoft Research Asia, tyliu@microsoft.com
  • Abstract: This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval.
  • In the first part of the tutorial, the authors will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications.
Highlights
  • Bio: Tie-Yan Liu is a lead researcher at Microsoft Research Asia
  • Tie-Yan Liu, Microsoft Research Asia, tyliu@microsoft.com. This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval
  • In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications
  • He leads a team working on learning to rank for information retrieval, and large-scale graph learning
Results
  • In the second part of the tutorial, the authors will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing.
  • The authors will briefly mention the recent advances in statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods.
  • The authors will conclude the tutorial and show several future research directions.
  • ACM Categories & Descriptors: H.3.3 [Information Search and Retrieval]: Retrieval models; I.2.6 [Learning]: Parameter learning.
  • General Terms: Algorithms, Experimentation, Theory.
  • Bio: Tie-Yan Liu is a lead researcher at Microsoft Research Asia.
  • He leads a team working on learning to rank for information retrieval, and large-scale graph learning.
  • He has more than 70 quality papers published in referred conferences and journals and over 50 filed US / international patents or pending applications.
  • 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 a Program Committee Co-Chair of RIAO (2010), an Area Chair of SIGIR (2008~2010) and AIRS (2009~2010), a Co-Chair of SIGIR workshop on learning to rank for IR (2007~2009), a Co-Chair of ICML workshop on learning to rank (2010), and a Program Committee member of many other international conferences such as WWW, ICML, KDD, WSDM, and ACL.
  • He is on the Editorial Board of the Information Retrieval Journal (IRJ), and is a guest editor of the special issue on learning to rank of IRJ.
Conclusion
  • He has given tutorials on learning to rank at several conferences including SIGIR 2008, WWW 2008, and WWW 2009.
  • Copyright is held by the author/owner(s).
  • SIGIR’10, July 19–23, 2010, Geneva, Switzerland.
  • ACM 978-1-60558-896-4/10/07.
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