A Cooperative Coevolution Framework for Parallel Learning to Rank

We propose CCRank, the first parallel framework for evolutionary algorithms (EA) based learning to rank, aiming to significantly improve learning efficiency while maintain accuracy. CCRank is based on cooperative coevolution (CC), a divide-andconquer 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 benchmarks in comparison with the state-of-the-art algorithms show the performance gains of CCRank in efficiency and accuracy.