PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Proactive information retrieval by monitoring eye movements
Samuel Kaski
In: BrainIT, The Second International Conference on Brain-Inspired Information Technology, 7-9 Oct 2005, Kitakyushu, Japan.

Abstract

A long term goal in user modeling for improving human-computer interaction is to understand the user's intent based on her monitored actions. We are developing an information retrieval system where the task is to predict relevance for new documents, given judgments on old ones. By monitoring the user's eye movements and inferring implicit feedback from them we reduce the amount of tedious ranking of retrieved documents, called relevance feedback in standard information retrieval. Relevance is inferred with machine learning methods, trained on eye movement patterns measured in settings where relevance is known. Noise in the predictions is compensated for by fusing the eye movements with other information about the user's preferences. The goal is to make the information retrieval system proactive, that is, capable of anticipating the user's interests.

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EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
User Modelling for Computer Human Interaction
ID Code:1699
Deposited By:Samuel Kaski
Deposited On:28 November 2005