PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining Eye Movements and Collaborative Filtering for Proactive Information Retrieval
Kai Puolamäki, Jarkko Salojärvi, Eerika Savia, Jaana Simola and Samuel Kaski
In: SIGIR 2005, 15-19 Aug 2005, Salvador, Brazil.


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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1195
Deposited By:Eerika Savia
Deposited On:24 November 2005