Towards an adaptive information retrieval system
Standard Information Retrieval Systems (IRS) can be used to retrieve information in response to specific requests, but they have no powers of adaption to particular users over repeated sessions. This paper describes a learning system which uses relevance feedback from a probabilistic IRS to incrementally evolve a context for a user, over a number of online sessions. We demonstrate the learning implementation with an example, and argue that it can help an IRS adapt to a user's specific needs, by using this context to influence document display and selection.
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