Publication Type

Conference Proceeding Article

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

Research Areas

Data Management and Analytics

Publication

Proceedings of the 25th AAAI Conference on Artificial Intelligence

First Page

1249

Last Page

1254

ISBN

9781577355076

Publisher

Association for the Advancement of Artificial Intelligence

City or Country

Menlo Park, CA

Additional URL

http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3563

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