Conference Proceeding Article
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.
Databases and Information Systems
Data Management and Analytics
Proceedings of the Twenty-fifth AAAI Conference on Artificial Intelligence: San Francisco, 7-11 August 2011
City or Country
Menlo Park, CA
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1523
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