A generic ranking function discovery framework by genetic programming for information retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Fan2003b,
  author =       "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
  title =        "A generic ranking function discovery framework by
                 genetic programming for information retrieval",
  journal =      "Information Processing and Management",
  year =         "2003",
  volume =       "40",
  number =       "4",
  pages =        "587--602",
  keywords =     "genetic algorithms, genetic programming, Information
                 retrieval; Ranking function, Text mining",
  DOI =          "doi:10.1016/j.ipm.2003.08.001",
  URL =          "http://filebox.vt.edu/users/wfan/paper/ARRANGER/ip&m2003.pdf",
  URL =          "http://www.sciencedirect.com/science/article/B6VC8-49J8S58-2/2/158a3713b59ef9defad7d00e81707f66",
  size =         "16 pages",
  abstract =     "Ranking functions play a substantial role in the
                 performance of information retrieval (IR) systems and
                 search engines. Although there are many ranking
                 functions available in the IR literature, various
                 empirical evaluation studies show that ranking
                 functions do not perform consistently well across
                 different contexts (queries, collections, users).
                 Moreover, it is often difficult and very expensive for
                 human beings to design optimal ranking functions that
                 work well in all these contexts. In this paper, we
                 propose a novel ranking function discovery framework
                 based on Genetic Programming and show through various
                 experiments how this new framework helps automate the
                 ranking function design/discovery process.",
}

Genetic Programming entries for Weiguo Fan Michael D Gordon Praveen Pathak

Citations