CCRank: Parallel Learning to Rank with Cooperative Coevolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Shuaiqiang Wang and Byron Gao and Ke Wang and 
                 Hady Lauw",
  title =        "{CCRank:} Parallel Learning to Rank with Cooperative
  booktitle =    "Proceedings of the Twenty-Fifth AAAI Conference on
                 Artificial Intelligence",
  year =         "2011",
  editor =       "Wolfram Burgard and Dan Roth",
  address =      "San Francisco, California USA",
  publisher_address = "Menlo Park, California, USA",
  month =        aug # " 7-11",
  organisation = "Association for the Advancement of Artificial
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "6 pages",
  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 optimisation for problems with large search
                 space and complex structures. Moreover, CC naturally
                 allows parallelisation 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.",
  notes =        "",

Genetic Programming entries for Shuaiqiang Wang Byron J Gao Ke Wang Hady W Lauw