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.
The main purpose of this workshop, in conjunction with SIGIR 2007, 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.
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
Several shared data sets have been released from Microsoft Research Asia (http://research.microsoft.com/users/tyliu/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.
We are organizing a second workshop on learning to rank at SIGIR 2008.
Papers should be submitted electronically via the submission site (https://cmt.research.microsoft.com/LR4IR2007/). 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; please anonymize your submission. All accepted papers will be published in the proceedings of the workshop. The proceedings will be printed and made available at the workshop.
Paper submission has been closed on June
8. The review process has started.
Benchmark Dataset for Research on Learning to Rank for
An Axiomatic Study of Learned Term-Weighting Schemes.
SoftRank: Optimising Non-Smooth Rank Metrics.
Learning to Rank with Pairwise Regularized Least-Squares.
Learning to Rank Documents for Ad-Hoc Retrieval with Regularized Models.
Learning to Rank for Information Retrieval Using Genetic Programming.
Addressing Malicious Noise in Clickthrough Data.
Efficient Query Delegation by Detecting Redundant Retrieval Strategies.
09:00~09:40: Keynote Speech I
Learning about Ranking and Retrieval Models
W. Bruce Croft
09:45~10:30: Paper Session I: Experience Sharing
a) LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval
Tie-Yan Liu, Jun Xu, Tao Qin, Wenying Xiong, Hang Li
b) An Axiomatic Study of Learned Term-Weighting Schemes
Ronan Cummins, Colm O’Riordan
10:30~11:00: Coffee break
11:00~12:30: Paper Session II: Algorithms
a) SoftRank: Optimising Non-Smooth Rank Metrics
Michael Taylor, John Guiver, Stephen Robertson, Tom Minka
b) Learning to Rank with Pairwise Regularized Least-Squares
Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jorma Boberg, Tapio Salakoski
c) Learning to Rank Documents for Ad-Hoc Retrieval with Regularized Models
Guihong Cao, Jian-Yun Nie, Luo Si, Jing Bai
d) Learning to Rank for Information Retrieval Using Genetic Programming
Jen-Yuan Yeh, Jung-Yi Lin, Hao-Ren Ke, Wei-Pang Yang
14:00~14:40: Keynote Speech II
Learning to Rank for Web Search: Some New Directions
Christopher J. C. Burges
14:45~15:30: Paper Session III: Applications
a) Addressing Malicious Noise in Clickthrough Data
b) Efficient Query Delegation by Detecting Redundant Retrieval Strategies
Christian Scheel, Nicolas Neubauer, Andreas Lommatzsch,
Klaus Obermayer, Sahin Albayrak
15:30~16:00: Coffee break
Thorsten Joachims, Cornell Univ.
Hang Li, Microsoft Research Asia
Tie-Yan Liu, Microsoft Research Asia
ChengXiang Zhai, Univ. of Illinois at Urbana-Champaign
Eugene Agichtein, Emory University
Javed Aslam, Northeastern University
Chris Burges, Microsoft Research
Olivier Chapelle, Yahoo Research
Hsin-Hsi, Chen, National University of Taiwan
Bruce Croft, University of Massachusetts, Amherst
Ralph Herbrich, Microsoft Research Cambridge
Djoerd Hiemstra, University of Twente
Thomas Hofmann, Google
Rong Jin, Michigan State University
Paul Kantor, Rutgers University
Sathiya Keerthi, Yahoo Research
Ravi Kumar, Yahoo Research
Quov Le, Australian National University
Guy Lebanon, Prudue University
Donald Metzler, University Massachusetts
Einat Minkov, Carnegie Mellon University
Filip Radlinski, Cornell University
Mehran Sahami, Google
Robert Schapire, Princeton University
Michael Taylor, Microsoft Research Cambridge
Yiming Yang, Carnegie Mellon University
Yi Zhang, University of California, Santa Cruz
Kai Yu, NEC Research Institute
Hongyuan Zha, Georgia Tech
tyliu [at] microsoft [dot] com