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9.1 Relevance feedback and pseudo relevance feedback The user issues a (short, simple) query. The system returns an initial set of retrieval results. The user marks some returned documents as relevant or nonrelevant. The system computes a better representation of the information need based on the user feedback.
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 a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query.
Relevance feedback can also have practical problems. The long queries that are generated by straightforward application of relevance feedback techniques are inefficient for a typical IR system. This results in a high computing cost for the retrieval and potentially long response times for the user.
Relevance feedback allows searchers to tell the search engine which results are and arent relevant, guiding the search engine better understand the query and thus improve the results. The simplest relevance feedback mechanisms involve direct, explicit feedback applied to the search results themselves.
In some systems user relevance feedback is used to tune the system in order to produce more meaningful results. This also helps to model human perception in a better way. For better understanding, an analogy is drawn between a document and a video sequence (refer Fig.
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In some systems user relevance feedback is used to tune the system in order to produce more meaningful results. This also helps to model human perception in a better way. For better understanding, an analogy is drawn between a document and a video sequence (refer Fig. 1).
Relevance feedback allows searchers to tell the search engine which results are and arent relevant, guiding the search engine better understand the query and thus improve the results. The simplest relevance feedback mechanisms involve direct, explicit feedback applied to the search results themselves.
Relevance feedback can be classified into two main types: explicit and implicit. Explicit feedback requires the user to explicitly indicate which documents are relevant or irrelevant to their query, usually by rating, ranking, or selecting them.

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