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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 formal study of relevance began in the 20th century with the study of what would later be called bibliometrics. In the 1930s and 1940s, S. C. Bradford used the term "relevant" to characterize articles relevant to a subject (cf., Bradford's law). In the 1950s, the first information retrieval systems emerged, and researchers noted the ...
Relevance feedback allows users to guide an IR system by indicating whether particular results are more or less relevant. [9] Summarization and analytics help users digest the results that come back from the query. Summarization here is intended to encompass any means of aggregating or compressing the query results into a more human-consumable ...
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
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 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 .
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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]