Information Retrieval Advances using Relevance Feedback

Relevance Feedback (RF) is a powerful technique whereby a user can instruct an Information Retrieval (IR) system to find additional relevant documents by providing relevance information on certain documents or query terms. There are several major design decisions that can affect how a RF system can help users. In this paper, I present a brief literature survey covering two emphases of RF: term selection and term weighting in the two prevalent IR models: probabilistic and vector-based. I then summarize how both the techniques and the underlying models can be combined and to what ends. Finally, I hypothesize why Relevance Feedback is not prevalent on World Wide Web search services, and give suggestions on which methods may be appropriate and what, if anything, should be done to add feedback techniques to Web searching.

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