Parallel learning to rank for information retrieval

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@InProceedings{Wang:2011:SIGIR,
  author =       "Shuaiqiang Wang and Byron J. Gao and Ke Wang and 
                 Hady W. Lauw",
  title =        "Parallel learning to rank for information retrieval",
  booktitle =    "Proceedings of the 34th international ACM SIGIR
                 conference on Research and development in Information",
  series =       "SIGIR '11",
  year =         "2011",
  isbn13 =       "978-1-4503-0757-4",
  address =      "Beijing, China",
  pages =        "1083--1084",
  numpages =     "2",
  URL =          "http://doi.acm.org/10.1145/2009916.2010060",
  DOI =          "doi:10.1145/2009916.2010060",
  acmid =        "2010060",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming: Poster,
                 cooperative coevolution, information retrieval,
                 learning to rank, mapreduce, parallel algorithms",
  abstract =     "Learning to rank represents a category of effective
                 ranking methods for information retrieval. While the
                 primary concern of existing research has been accuracy,
                 learning efficiency is becoming an important issue due
                 to the unprecedented availability of large-scale
                 training data and the need for continuous update of
                 ranking functions. In this paper, we investigate
                 parallel learning to rank, targeting simultaneous
                 improvement in accuracy and efficiency.",
}

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

Citations