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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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