CCRank: Parallel Learning to Rank with Cooperative Coevolution


  • Shuaiqiang Wang Shandong University of Finance
  • Byron Gao Texas State University-San Marcos
  • Ke Wang Simon Fraser University
  • Hady Lauw Institute for Infocomm Research



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.




How to Cite

Wang, S., Gao, B., Wang, K., & Lauw, H. (2011). CCRank: Parallel Learning to Rank with Cooperative Coevolution. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1249-1254.