CCRank: Parallel Learning to Rank with Cooperative Coevolution

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@InProceedings{Wang.Shuaiqiang:2011:AAAI,

• author = "Shuaiqiang Wang and Byron Gao and Ke Wang and Hady Lauw",
• title = "{CCRank:} Parallel Learning to Rank with Cooperative Coevolution",
• 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 Intelligence",
• publisher = "AAAI Press",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3563",
• URL = "http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3563/4060.pdf",
• 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 = "http://www.aaai.org/Conferences/AAAI/aaai11.php",

}

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

Citations