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

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