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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 ...
Traditional evaluation metrics, designed for Boolean retrieval [clarification needed] or top-k retrieval, include precision and recall. All measures assume a ground truth notion of relevance: every document is known to be either relevant or non
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 .
Presented with a list of documents in response to a search query, an experiment participant is asked to judge the relevance of each document to the query. Each document is to be judged on a scale of 0-3 with 0 meaning not relevant, 3 meaning highly relevant, and 1 and 2 meaning "somewhere in between".
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House Republicans are scrambling to find a way to avoid a government shutdown after their second funding proposal was rejected, and are now planning to vote on a third proposal soon.
A cleaning company has been fined $171,000 after federal investigators found 11 children working a "dangerous" overnight shift at a meat processing plant in Iowa.