Relevance feedback and pseudo relevance feedback

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:

• The user issues a (short, simple) query.
• The system returns an initial set of retrieval results.
• The user marks some returned documents as relevant or nonrelevant.
• The system computes a better representation of the information need based on the user feedback.
• The system displays a revised set of retrieval results.

Relevance feedback can go through one or more iterations of this sort. The process exploits the idea that it may be difficult to formulate a good query when you don't know the collection well, but it is easy to judge particular documents, and so it makes sense to engage in iterative query refinement of this sort. In such a scenario, relevance feedback can also be effective in tracking a user's evolving information need: seeing some documents may lead users to refine their understanding of the information they are seeking.

(a)

(b)

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.html

the 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.