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Using \RelFbk for query refinement

While Figure (FOAref) showed the retrieved set of documents as a simple set, it is interesting to impose the RelFbk labeling on the retrieved documents viewed in vector space. Figure (figure) shows a query vector and a number of retrieved documents, together with a plausible distribution of RelFbk over them. That is, we can expect that there is some localized region in vector space where \blue{$\oplus$} relevant documents are most likely to occur. If we believe that these positively labeled retrieved documents are in fact clustered, it becomes appropriate to consider a hypothetical CENTROID (average) document $d^{+}$ which is at the center of all those documents that users have identified as relevant.

It is less reasonable, however, to imagine that the {negatively} labeled \red{$\ominus$} documents are similarly clustered. Documents which were inappropriately retrieved failed to be relevant for one reason or another. There might be a number of such reasons. These are shown as \green, discriminating planes in Figure (figure) .

The vector space view also lets us easily portray two quite different uses to which RelFbk information might be applied. Most typically, RelFbk is used to refine the user's query. Figure (figure) represents this refinement in terms of two changes we can make to the initial query vector. The first of these is to ``take a step towards'' the centroid of the positive RelFbk cluster. The size of this step$ is treated as the right answer, the parameter alpha becomes analogous to neural net's learning rate.} reflects how confident we are that the positive cluster centroid is a better characterization of the user's interests than their original query.

There is one important difference between the query and even a slight perturbation of it towards a cluster's centroid: While the original query is often very sparse and resulting from just those few words used in the user's original query, any movement towards the centroid will include (linear combinations of) keywords used in any of the positively labeled documents. The additional difficulty in implementing this much more densely-filled feature vector becomes a serious obstacle in many system implementations. The fact that refined queries involve many more non-zero keyword entries also means that query weighting and matching techniques may be sensitive to this difference.

Seminal work on the use of RelFbk was done by Salton, especially with students Roccio, Brauen and Ide in the early 1970s [Rocchio66] [REF567] [REF319] . More recent students have extended the theory of query refinement, and related it to topics in machine learning [Buckley94] [Buckley95] [Allan96] .

Some of these experiments explored a second modification to the query vector. In addition to moving towards the $d^{+}$ center of \blue{$\oplus$}, it is also plausible to move away from the irrelevant retrieved documents \red{$\ominus$}. As noted above, however, it is much less likely that these irrelevant documents are as conveniently clustered. As Salton [Salton83] reports: ... retrieval operation is most effective when the relevant documents as well as the non-relevant documents are tightly clustered and the difference between the two groups is as large as possible. .... The RelFbk operation is less favorable in the more realistic case where the set of non-relevant documents covers a wider area of the space. [p. 145] One possible strategy is to take a single element $d^{-}$ of the irrelevant retrieved documents (for example, the most highly ranked irrelevant retrieval) and define the direction of movement with respect to it alone.

As we have discussed above in connection with Figure (FOAref) , RelFbk helps to link together individual queries into a browsing sequence. And so, while we have focused here on the the simplest form of query refinement, with respect to the users' initial queries, RelFbk can be given again and again. An initial query vector is moved towards the centroid of documents identified as relevant (perhaps away from an especially bad one), this modified query instigates a new retrieval which is again refined. In practice, it appears that such adjustments result in diminishing returns after only a few iterations of query refinement [Salton83] [Stanfill86] .

However, Section §7.3 will discuss a type of FOA in which a document corpus is constantly changing and the user's interest in a topic is long-lived. In this case, we can imagine the query as a FILTER against which a constant stream (e.g., of newly published Web documents) is applied. RelFbk has also been used in this setting, to make ongoing changes to the query/filter that continue to improve its effectiveness [Allan96] .

Using RelFbk for query refinement produces results which are immediately satisfying to the users. First, it automatically generates a new query with which they can continue their browsing process. Second, the statistical analysis of positively labeled retrieved documents can provide other forms of useful information to the users as well. For example, rather than simply retrieving a new set of documents, new {\em keywords} not originally in the users' queries but present in positive documents at statistically significantly levels, can be suggested to the users as new vocabulary. Conversely, words that were in the original query but {\em negatively} correlated with $d^{+}$ (and/or positively correlated with $d^{-}$) can be identified as well.

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