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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 ...
Following is an approach to determine and name degrees of relevance and how to utilize the results: Relevance level "High" – The highest relevance is objective information directly about the topic of the article. "John Smith is a member of the XYZ organization" in the "John Smith" article is an example of this.
The topics an article covers should match the article's title. An article titled Internet should be about the global computer network, not about networking, software, or computers in general. The way in which those subjects relate to the Internet should be described, but all other information about them should be put in networking , software ...
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 ...
When not obvious, relevance is decided by the editors of the article, based on what is considered likely to be useful to readers. The give and take between editors functions as a social-engine for discerning relevance. Wikipedia policy maintains the health of that social engine but does not itself act as an engine for discerning relevance.
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.)
Article feedback was found at the bottom of many Wikipedia articles; it is a simple form that readers can use to submit suggestions for improvement. (See screenshot below.) These suggestions are then reviewed by Wikipedia contributors, who can identify and take action on useful feedback -- while ignoring or removing bad submissions.
Once relevance levels have been assigned to the retrieved results, information retrieval performance measures can be used to assess the quality of a retrieval system's output. In contrast to this focus solely on topical relevance, the information science community has emphasized user studies that consider user relevance. [3]