WebSail: From On-line Learning to Web Search

被引：3
|
作者：
Chen, Zhixiang [1 ]
Meng, Xiannong [2 ]
Zhu, Binhai [3 ]
Fowler, Richard H. [1 ]
机构：
[1] Department of Computer Science, University of Texas-Pan American, Edinburg,TX, United States
[2] Department of Computer Science, Bucknell University, Lewisburg,PA, United States
[3] Department of Computer Science, Montana State University, Bozeman,MT, United States
来源：
Knowledge and Information Systems | 2002年 / 4卷 / 02期
关键词：
Adaptive learning - Document ranking - Document ranking; - Keyword: adaptive learning; - Online learning - Real- time - Relevance feedback - Relevance feedback; - Vector space; - Web searches;
D O I：
10.1007/s101150200005
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：219 / 227
页数：8
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