We introduce a new search strategy, in which the information
retrieval (IR) query is inferred from eye movements measured when
the user is reading text during an IR task. In training phase, we
know the users' interest, that is, the relevance of training
documents. We learn a predictor that produces a ``query'' given the
eye movements; the target of learning is an ``optimal'' query that
is computed based on the known relevance of the training documents.
Assuming the predictor is universal with respect to the users'
interests, it can also be applied to infer the implicit query when
we have no prior knowledge of the users' interests. The result of an
empirical study is that it is possible to learn the implicit query
from a small set of read documents, such that relevance predictions
for a large set of unseen documents are ranked significantly better
than by random guessing.
This work was supported in part by the IST Programme of
the European Community, under the PASCAL Network of Excellence,
IST-2002-506778. This publication only reflects the authors views. All
rights are reserved because of other commitments.