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Queries subsequent to the first are performed much differently. After
AIR is done retrieving the network of features, the user responds with
indicating which features are considered (by that user) relevant to the
query and which are not. Using a mouse, the user marks features with the
symbols: {\bf ++, +, --} and {\bf -- --}, indicating that the feature
was {\tt Very Relevant, Relevant, Irrelevant}, or {\tt Very Irrelevant},
respectively. Not all features need be commented upon.
The system
constructs a new query directly from this feedback. First, terms from
the previous query are retained. Positively marked features are added to
this query, as are the negated versions of features marked negatively.
Equal weight is placed on each of these features, except that features
marked {Very Relevant} or {\tt Very Irrelevant} are counted double.
From
the perspective of retrieval, this RelFbk becomes a form of {\em
browsing}: positively marked features are directions which the user
wants to pursue, and negatively marked features are directions which
should be pruned from the search. From the perspective of learning,
thisRelFbk is exactly the training signal AIR needs to modify its
representations through learning. This unification of learning (i.e.,
changing representations) and doing (i.e., browsing) was a central
component of AIR's design. It mean that the collection of feedback is
not an onerous, additional task for the user, but a natural part of the
retrieval process.
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\RelFbk in AIR