Conference proceeding
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, p.8 pp
2004
DOI: 10.1109/HICSS.2004.1265279
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 obtained by support vector machine.
Details
- Title: Subtitle
- Ranking function optimization for effective Web search by genetic programming: an empirical study
- Creators
- Weiguo Fan - Virginia TechM.D. GordonP. Pathak - University of Florida Health Science CenterWensi Xi - Virginia TechE.A. Fox - Virginia Tech
- Resource Type
- Conference proceeding
- Publication Details
- 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the, p.8 pp
- Publisher
- IEEE
- DOI
- 10.1109/HICSS.2004.1265279
- Language
- English
- Date published
- 2004
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380529702771
Metrics
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