A GP-adaptive web ranking discovery framework based on combinative content and context features

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@Article{Keyhanipour2009,
  author =       "Amir Hosein Keyhanipour and Maryam Piroozmand and 
                 Kambiz Badie",
  title =        "A GP-adaptive web ranking discovery framework based on
                 combinative content and context features",
  journal =      "Journal of Informetrics",
  year =         "2009",
  volume =       "3",
  number =       "1",
  pages =        "78--89",
  month =        jan,
  ISSN =         "1751-1577",
  DOI =          "DOI:10.1016/j.joi.2008.11.006",
  URL =          "http://www.sciencedirect.com/science/article/B83WV-4V99602-2/2/dbdb4475cf1bfdaf20f775edd1aa4636",
  keywords =     "genetic algorithms, genetic programming, Document
                 ranking, Classifier designing, LETOR, LAGEP",
  abstract =     "The problem of ranking is a crucial task in the web
                 information retrieval systems. The dynamic nature of
                 information resources as well as the continuous changes
                 in the information demands of the users has made it
                 very difficult to provide effective methods for data
                 mining and document ranking. Regarding these
                 challenges, in this paper an adaptive ranking algorithm
                 is proposed named GPRank. This algorithm which is a
                 function discovery framework, uses the relatively
                 simple features of web documents to provide suitable
                 rankings using a multi-layer/multi-population genetic
                 programming architecture. Experiments done, illustrate
                 that GPRank has better performance in comparison with
                 well-known ranking techniques and also against its full
                 mode edition.",
}

Genetic Programming entries for Amir Hosein Keyhanipour Maryam Piroozmand Kambiz Badie

Citations