We study a new task, proactive information retrieval by combining
implicit relevance feedback and collaborative filtering. We have
constructed a controlled experimental setting, a prototype
application, in which the users try to find interesting scientific
articles by browsing their titles. Implicit feedback is inferred from
eye movement signals, with discriminative hidden Markov models
estimated from existing data in which explicit relevance feedback is
available. Collaborative filtering is carried out using the User Rating
Profile model, a state-of-the-art probabilistic latent
variable model, computed using Markov Chain Monte Carlo techniques.
For new document titles the prediction accuracy with eye movements,
collaborative filtering, and their combination was significantly
better than by chance. The best prediction accuracy still leaves
room for improvement but shows that proactive information retrieval
and combination of many sources of relevance feedback is feasible.
This work was supported by the Academy of Finland, decision
no. 79017, and by the IST Programme of the European
Community, under the PASCAL Network of Excellence, IST-2002-506778.