Ranking Function Optimization For Effective Web Search By Genetic Programming: An Empirical Study

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

@InProceedings{Fan2004,
  author =       "Weiguo Fan and Michael D. Gordon and 
                 Praveen Pathak and Wensi Xi and Edward A. Fox",
  title =        "Ranking Function Optimization For Effective Web Search
                 By Genetic Programming: An Empirical Study",
  booktitle =    "Proceedings of 37th Hawaii International Conference on
                 System Sciences",
  year =         "2004",
  pages =        "105--112",
  address =      "Hawaii",
  month =        "5-8 " # jan,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/HICSS.2004.1265279",
  size =         "8 pages",
  abstract =     "Web search engines have become indispensable in our
                 daily life to help us find the information we need.
                 Although search engines are very fast in search
                 response time, their effectiveness in finding useful
                 and relevant documents at the top of the search hit
                 list needs to be improved. In this paper, we report our
                 experience applying Genetic Programming (GP) to the
                 ranking function discovery problem leveraging the
                 structural information of HTML documents. Our empirical
                 experiments using the web track data from recent TREC
                 conferences show that we can discover better ranking
                 functions than existing well-known ranking strategies
                 from IR, such as Okapi, Ptfidf. The performance is even
                 comparable to those",
  notes =        "http://filebox.vt.edu/users/wfan/pub_area.html",
}

Genetic Programming entries for Weiguo Fan Michael D Gordon Praveen Pathak Wensi Xi Edward A Fox

Citations