Submission deadline has been extended to May 23 (23:59, Hawaii time), per many people's requests.
The task of
"learning to rank" has emerged as an active and growing area
of research both in information retrieval and machine
learning. The goal is to design and apply methods to
automatically learn a function from training data, such that
the function can sort objects (e.g., documents) according to
their degrees of relevance, preference, or importance as
defined in a specific application.
The relevance of this task for IR is without question,
because many IR problems are by nature ranking problems.
Improved algorithms for learning ranking functions promise
improved retrieval quality and less of a need for manual
parameter adaptation. In this way, many IR technologies can
be potentially enhanced by using learning to rank
techniques.
A workshop entitled Learning to Rank for Information
Retrieval will be organized at SIGIR 2008, following a very
successful workshop on the same topic at SIGIR 2007
(website:
http://research.microsoft.com/users/LR4IR-2007/
report:
http://delivery.acm.org/10.1145/1330000/1328974/p58-joachims.pdf?key1=1328974&key2=3383457021&coll=GUIDE&dl=&CFID=62683274&CFTOKEN=33141712
). The main purpose of this workshop is to bring together IR
researchers and ML researchers working on or interested in
the technologies, and let them to share their latest
research results, to express their opinions on the related
issues, and to discuss future directions.
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
Several shared data sets have been released from Microsoft Research Asia (http://research.microsoft.com/users/LETOR/). The data sets, created based on OHSUMED and TREC data, contain features and relevance judgments for training and evaluation of learning to rank methods. It is encouraged to use the data sets to conduct experiments in the submissions to the workshop.
Papers should be submitted electronically via the submission site (https://cmt.research.microsoft.com/LR4IR2008/). 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.
To be announced.
The one day workshop consists of technical presentations, invited speeches, and an opinion & commentary session. Details will be announced later.
Hang Li, Microsoft Research Asia
Tie-Yan Liu, Microsoft Research Asia
ChengXiang Zhai, Univ. of Illinois at Urbana-Champaign
Alekh Agarwal, University of
California at Berkeley
Djoerd Hiemstra, University of Twente
Donald Metzler, University Massachusetts
Einat Minkov, Carnegie Mellon University
Filip Radlinski, Cornell University
Guirong Xue, Shanghai Jiao-Tong University
Guy Lebanon, Prudue University
Hongyuan Zha, Georgia Tech
Hsin-Hsi Chen, National University of Taiwan
Irina Matveeva, University of Chicago
Javed Aslam, Northeastern University
John Guiver, Microsoft Research Cambridge
Jun Xu, Microsoft Research Asia
Kai Yu, NEC Research Institute
Michael Taylor, Microsoft Research Cambridge
Olivier Chapelle, Yahoo Research
Ping Li, Cornell University
Quoc Le, Australian National University
Ralph Herbrich, Microsoft Research Cambridge
Ravi Kumar, Yahoo Research
Tao Qin, Tsinghua University
Yisong Yue, Cornell university
Zhaohui Zheng, Yahoo Research
Soumen Chakrabarti, Indian Institute of Technology
Tie-Yan Liu, Microsoft Research Asia
tyliu [at] microsoft [dot] com