WebSail: From On-line Learning to Web Search

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.