A Cooperative Coevolution Framework for Parallel Learning to Rank

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@Article{Wang:2015:ieeeKDE,

• author = "Shuaiqiang Wang and Yun Wu and Byron J. Gao and Ke Wang and Hady W. Lauw and Jun Ma",
• title = "A Cooperative Coevolution Framework for Parallel Learning to Rank",
• journal = "IEEE Transactions on Knowledge and Data Engineering",
• year = "2015",
• volume = "27",
• number = "12",
• pages = "3152--3165",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Cooperative coevolution, learning to rank, information retrieval, immune programming",
• ISSN = "1041-4347",
• DOI = "doi:10.1109/TKDE.2015.2453952",
• size = "14 pages",
• abstract = "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 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 benchmark datasets in comparison with the state-of-the-art algorithms show the performance gains of CCRank in efficiency and accuracy.",
• notes = "Also known as \cite{7152946}",

}

Genetic Programming entries for Shuaiqiang Wang Yun Wu Byron J Gao Ke Wang Hady W Lauw Jun Ma

Citations