The idea of relevance feedback ( ) is to involve the user in the retrieval process so as to improve the final result set. In particular, the user gives feedback on the relevance of documents in an initial set of results. The basic procedure is:
Relevance feedback searching over images.(a) The user views the initial query results for a query of bike, selects the first, third and fourth result in the top row and the fourth result in the bottom row as relevant, and submits this feedback. (b) The users sees the revised result set. Precision is greatly improved. From http://nayana.ece.ucsb.edu/imsearch/imsearch.html(Newsam et al., 2001).
Image search provides a good example of relevance feedback. Not only is it easy to see the results at work, but this is a domain where a user can easily have difficulty formulating what they want in words, but can easily indicate relevant or nonrelevant images. After the user enters an initial query for bike on the demonstration system at:
http://nayana.ece.ucsb.edu/imsearch/imsearch.htmlthe initial results (in this case, images) are returned. In Figure 9.1 (a), the user has selected some of them as relevant. These will be used to refine the query, while other displayed results have no effect on the reformulation. Figure 9.1 (b) then shows the new top-ranked results calculated after this round of relevance feedback.
Figure 9.2 shows a textual IR example where the user wishes to find out about new applications of space satellites.