Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
8-2011
Abstract
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed subproblems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms show that CCRank gains in both accuracy and efficiency.
Discipline
Databases and Information Systems
Publication
Proceedings of the Twenty-fifth AAAI Conference on Artificial Intelligence: San Francisco, 7-11 August 2011
First Page
1249
Last Page
1254
ISBN
9781577355076
Publisher
AAAI
City or Country
Menlo Park, CA
Citation
WANG, Shuaiqiang; GAO, Byron J.; WANG, Ke; and LAUW, Hady W..
CCRank: Parallel Learning to Rank with Cooperative Coevolution. (2011). Proceedings of the Twenty-fifth AAAI Conference on Artificial Intelligence: San Francisco, 7-11 August 2011. 1249-1254.
Available at: https://ink.library.smu.edu.sg/sis_research/1523
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3563