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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 "Lower" – Information that is "twice removed" should usually not be included unless the other considerations described above are unusually strong. For example, in the above "John Smith" article, "Murderer Larry Jones was also a member of the XYZ organization."
Toggle Establishing relevance subsection. 4.1 Impact. 4.2 Fundamental information. 4.3 Distinguishing traits. 4.4 Context. 5 Connections between subjects.
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]
However, some statements in the article are biased. Please remember that all articles must be written from a neutral point of view. Here are some tips to help you write unbiased articles." If you regularly respond to requests for feedback here, please consider adding the following userbox to your userpage: {{Wikipedia:Requests for feedback ...
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 .
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.
2.1 Practical realities of "relevancy" in Wikipedia. 2.2 Guiding principles. 2.2.1 Content must be about the subject of the article.