Abstract
We investigate the applicability of on-line learning algorithms to the real-world problem of Web search. Consider that Web documents are indexed using n Boolean features. We first present a practically efficient online learning algorithm TW2 to search for Web documents represented by a disjunction of at most k relevant features. We then design and implement WebSail, a real-time adaptive Web search learner, with TW2 as its learning component. WebSail learns from the user's relevance feedback in real-time and helps the user to search for the desired Web documents. The architecture and performance of WebSail are also discussed.
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