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WebSail: From On-line Learning to Web Search |
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| Authors: | Zhixiang Chen Xiannong Meng Binhai Zhu Richard H. Fowler |
| Affiliation: | (1) Department of Computer Science, University of Texas-Pan American, Edinburg, Texas, USA, US;(2) Department of Computer Science, Bucknell University, Lewisburg, Pennsylvania, USA, US;(3) Department of Computer Science, Montana State University, Bozeman, Montana, USA, US |
| Abstract: | In this paper we report our research on building WebSail, an intelligent web search engine that is able to perform real-time adaptive learning. WebSail learns from the user's relevance feedback, so that it is able to speed up its search process and to enhance its search performance. We design an efficient adaptive learning algorithm TW2 to search for web documents. WebSail employs TW2 together with an internal index database and a real-time meta-searcher to perform real-time adaptive learning to find desired documents with as little relevance feedback from the user as possible. The architecture and performance of WebSail are also discussed. Received 3 November 2000 / Revised 13 March 2001 / Accepted in revised form 17 April 2001 |
| Keywords: | : Adaptive learning Document ranking Relevance feedback Vector space Web search |
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