WORKSHOP REPORT Learning to Rank for Information Retrieval (LR4IR 2009) Hang Li Microsoft Research Asia hangli@microsoft.com Tie-Yan Liu Microsoft Research Asia tyliu@microsoft.com ChengXiang Zhai University of Illinois at Urbana-Champaign czhai@cs.uiuc.edu Overview As an interdisciplinary field between information retrieval and machine learning, learning to rank is concerned with automatically constructing a ranking model using training data. Learning to rank technologies have been successfully applied to many tasks in information retrieval such as search and collaborative filtering, and have been attracting more and more attention recently. At SIGIR 2007 and SIGIR 2008, we have successfully organized two workshops on learning to rank for information retrieval with very good attendance. The reports of those two workshops can be found at http://www.sigir.org/forum/2007D/2007d_sigirforum_joachims.pdf http://www.sigir.org/forum/2008D/sigirwksp/2008d_sigirforum_li.pdf From the experiences of running those two workshops, we have found that there is a community emerging, consisting of people from both academia and industry and including both researchers and practitioners. They have rich experiences of IRand machine learning, and are also deeply interested in the learning to rank technologies. We have, therefore, organized a workshop on the same theme again, in conjunction with SIGIR 2009. The main purpose remains to bring together IR researchers and ML researchers working
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Learning to rank for information retrieval (LR4IR 2009)
Li, Hang; Liu, Tie-Yan; Zhai, ChengXiang
Follow ACM SIGIR Forum
, Volume 43 (2)
Association for Computing Machinery – Dec 14, 2009
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