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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 a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types ...
This is the so called pseudo-relevance feedback (PRF). [6] Pseudo-relevance feedback is efficient in average but can damage results for some queries, [7] especially difficult ones since the top retrieved documents are probably non-relevant. Pseudo-relevant documents are used to find expansion candidate terms that co-occur with many query terms. [8]
The Rocchio algorithm is based on a method of relevance feedback found in information retrieval systems which stemmed from the SMART Information Retrieval System developed between 1960 and 1964. Like many other retrieval systems, the Rocchio algorithm was developed using the vector space model .
For this example, that ordering would be the monotonically decreasing sort of all known relevance judgments. In addition to the six from this experiment, suppose we also know there is a document D 7 {\displaystyle D_{7}} with relevance grade 3 to the same query and a document D 8 {\displaystyle D_{8}} with relevance grade 2 to that query.
The number of relevant documents, , is used as the cutoff for calculation, and this varies from query to query. For example, if there are 15 documents relevant to "red" in a corpus (R=15), R-precision for "red" looks at the top 15 documents returned, counts the number that are relevant r {\displaystyle r} turns that into a relevancy fraction: r ...
The (standard) Boolean model of information retrieval (BIR) [1] is a classical information retrieval (IR) model and, at the same time, the first and most-adopted one. [2] The BIR is based on Boolean logic and classical set theory in that both the documents to be searched and the user's query are conceived as sets of terms (a bag-of-words model).
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A measure called "maximal marginal relevance" (MMR) has been proposed to manage this shortcoming. It considers the relevance of each document only in terms of how much new information it brings given the previous results. [13] In some cases, a query may have an ambiguous interpretation, or a variety of potential responses.