Information Retrieval by Inferring Implicit Queries from Eye Movements
David Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki and Samuel Kaski
In: Eleventh International Conference on Artificial Intelligence and Statistic, March 21-24, 2007, San Juan, Puerto Rico.
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. We learn a predictor
which links eye movements related to a term to the role of that term
in the query. Assuming this 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.