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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 ...
Relevance level "Medium" – Information that is "once removed" is less directly relevant, should receive a higher level of scrutiny and achieve higher levels in other areas (such as neutrality, weight and strength [further explanation needed] and objectivity of the material and sourcing) before inclusion, but may still may be sufficiently ...
This page in a nutshell: Wikipedia, by its nature, is an engine for discerning relevance. On Wikipedia, relevance is simply whether a fact is in the right article, based on whether it pertains to the article's subject.
Its purpose is to help editors improve the article based on reader feedback. To see the feedback page for this test sample, click on “Talk” at the top of the article page; then click on “View reader feedback” at the top of the talk page. For example, take a look at the feedback page for the Golden-crowned Sparrow. (Tech note: feedback ...
You can view feedback in a number of places: This central feedback page for all of Wikipedia; This sample article feedback page; On other articles with feedback, (Look for a link on these article talkpages to see feedback. Note that only about 10 percent of articles have feedback so far.)
Its purpose is to help editors improve the article based on reader feedback. To see the feedback page for this test sample, click on “Talk” at the top of the article page; then click on “View reader feedback” at the top of the talk page. For example, take a look at the feedback page for the Golden-crowned Sparrow.
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 ...
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