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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 ...
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 .
Machine learning based query term weight and synonym analyzer for query expansion. LucQE - open-source, Java. Provides a framework along with several implementations that allow to perform query expansion with the use of Apache Lucene. Xapian is an open-source search library which includes support for query expansion; ReQue open-source, Python ...
Relevance (information retrieval) – Measure of a document's applicability to a given subject or search query; Relevance feedback – type of feedback; Rocchio classification – A classification model in machine learning based on centroids; Search engine indexing – Method for data management
The following table lists the various web template engines used in Web template systems and a brief rundown of their features. Engine (implementation) [ a ] Languages [ b ]
We can generalize the previous 2D extended Boolean model example to higher t-dimensional space using Euclidean distances. This can be done using P-norms which extends the notion of distance to include p-distances, where 1 ≤ p ≤ ∞ is a new parameter.
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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.