A Cooperative Coevolution Framework for Parallel Learning to Rank

Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
2015 IEEE Transactions on Knowledge and Data Engineering  
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
more » ... ntially 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.
doi:10.1109/tkde.2015.2453952 fatcat:gltmjpyaencx5dnulatxppwyyy