A Cooperative Coevolution Framework for Parallel Learning to Rank

doi:10.1109/TKDE.2015.2453952

A Cooperative Coevolution Framework for Parallel Learning to Rank

2015-12-01

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DOI：10.1109/TKDE.2015.2453952

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We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. 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 sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in comparison with the state-of-the-art algorithms show the performance gains of CCRank in efficiency and accuracy.

Keywords:

Cooperative coevolution

learning to rank

information retrieval

genetic programming

immune programming

A Cooperative Coevolution Framework for Parallel Learning to Rank

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