Relevance feedback
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Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from some query and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or "pseudo" feedback.
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[edit] Explicit feedback
Explicit feedback is obtained by having the user mark specific documents as relevant or irrelevant.
[edit] Implicit feedback
Implicit feedback is inferred from user behavior, such as noting which documents they do and do not select for viewing, and/or how long they view those documents.
[edit] Blind feedback
Blind or "pseudo" relevance feedback is obtained by assuming that the top n documents in the result set actually are relevant.
[edit] Using relevance information
Relevance information is utilized by using the contents of the relevant documents to either adjust the weights of terms in the original query, or by using those contents to add words to the query. Relevance feedback is often implemented using the Rocchio algorithm.
[edit] Further reading
Relevance feedback lecture notes - Jimmy Lin's lecture notes, adapted from Doug Ouard's [1] - chapter from modern information retrieval
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