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37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the (2004)
Big Island, Hawaii
Jan. 5, 2004 to Jan. 8, 2004
ISSN: 1530-1605
ISBN: 0-7695-2056-1
pp: 40105
Weiguo Fan , Virginia Tech
Michael D. Gordon , University of Michigan
Praveen Pathak , University of Florida
Wensi Xi , Virginia Tech
Edward A. Fox , Virginia Tech
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, Pt.df. The performance is even comparable to those obtained by Support Vector Machine.
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CITATION

M. D. Gordon, E. A. Fox, W. Fan, P. Pathak and W. Xi, "Ranking Function Optimization for Effective Web Search by Genetic Programming: An Empirical Study," 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the(HICSS), Big Island, Hawaii, 2004, pp. 40105.
doi:10.1109/HICSS.2004.1265279
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