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 summarization, and have been attracting more and more attention recently in the information retrieval and machine learning communities.
At SIGIR 2007 and SIGIR 2008, we have successfully organized two workshops on learning to rank for information retrieval. The reports of those two workshops can be found at http://www.sigir.org/forum/2007D/2007d_sigirforum_joachims.pdf and http://www.sigir.org/forum/2008D/sigirwksp/2008d_sigirforum_li.pdf. You can also find the website of our previous workshops at http://research.microsoft.com/users/LR4IR-2007/ , http://research.microsoft.com/users/LR4IR-2008/.
Topics of Interests
We solicit submissions on any aspect of learning to rank for information retrieval. Particular areas of interest include, but are not limited to:
l Models, features, and algorithms of learning to rank
l Evaluation methods for learning to rank
l Data creation methods for learning to rank
l Applications of learning to rank methods to information retrieval
l Comparison between traditional approaches and learning approaches to ranking
l Theoretical analyses on learning to rank
l Empirical comparison between learning to rank methods
Shared Benchmark Data
During the workshop, the LETOR team will announce the version 4.0 of the LETOR dataset, which contains more queries, and enables new research topics.
8:30-8:40: Opening remarks
8:40-9:40: Invited Talk - Learning to rank for diversity (Paul B. Kantor, Rutgers University)
9:40-10:30: Paper Session 1 (2 papers) - learning to rank methods
Efficient and
Accurate Local Learning for Ranking
Learning to rank with low
rank
10:30-11:00 break
11:00-12:15: Paper session 2 (3 papers) - learning to rank applications
Learning to Rank QA Data
Ranking Experts with
Discriminative Probabilistic Models
Priors in Web Search
12:15-12:30: LETOR 4.0 announcement
12:30-1:30: Lunch
1:30-2:30: Invited talk - Direct Optimization for Ranking (Olivier Chapelle, Yahoo! Research)
2:30-3:00: LETOR feedback (for future versions)
3:00-3:30: Break
3:30-4:20: Paper session 3 (2 papers) - Evaluation of learning to rank
Is learning to rank effective for Web search
On the Choice of
Effectiveness Measures for Learning to Rank
4:20-5:20: Opinion session
5:20-5:30: Wrap up
Papers should be submitted electronically via the submission site (
https://cmt.research.microsoft.com/LR4IR2009/). Submitted papers should be in the ACM Conference style, see the ACM template page, and may not exceed 8 pages. All submissions will be reviewed by at least three members of the program committee. The review is double-blind; authors should conceal their identity where it is practical to do so. All accepted papers will be published in the proceedings of the workshop. The proceedings will be printed and made available at the workshop. At least one author should register and attend the workshop.Hang Li, Microsoft Research Asia
Tie-Yan Liu, Microsoft Research Asia
ChengXiang Zhai, Univ. of Illinois at Urbana-Champaign
Olivier Chapelle, Yahoo Research
Hsin-Hsi Chen, National University of Taiwan
Ralf Herbrich, Microsoft Research Cambridge
Rong Jin, Michigan State University
Sathiya Keerthi, Yahoo Research
Ravi Kumar, Yahoo Research
Guy Lebanon, Prudue University
Donald Metzler, Yahoo! Research
Einat Minkov, Carnegie Mellon University
Quoc Le, Stanford University
Filip Radlinski, Microsoft Research Cambridge
Michael Taylor, Microsoft Research Cambridge
Kai Yu, NEC Research Institute
Hongyuan Zha, Georgia Tech
Zhaohui Zheng, Yahoo Research
John Guiver, Microsoft Research Cambridge
Guirong Xue, Shanghai Jiao-Tong University
Alekh Agarwal, University of California at Berkeley
Soumen Chakrabarti, IIT Bombay
Ping Li, Cornell University
Irina Matveeva, University of Chicago
Yisong Yue, Cornell university
Jun Xu, Microsoft Research Asia
Tao Qin, Microsoft Research Asia
Tie-Yan Liu, Microsoft Research Asia
tyliu [at] microsoft [dot] com