Proactive Information Retrieval by User Modeling from Eye Tracking

In this position paper we review the results of the the eye-tracking -related part of the PRIMA project (Proactive Information Retrieval by Adaptive Models of User’s Attention and Interests), carried out during 2003–2005. The project focused on how to construct and combine user models from implicit or explicit feedback signals. If proper user models can be constructed, it will be possible to build proactive applications, that is, applications that learn to anticipate the user’s needs. Our prototype application is information retrieval, where implicit feedback signal is measured from eye movements. Relevance of read text is extracted from the feedback signal with hidden Markov models learned from a collected data set. Since relevance in general is hard to define, we have constructed an experimental setting where relevance is known a priori. The implicit feedback signal is very noisy. Thus, it needs to be supplemented with relevance predictions from other available sources. In the prototype application an alternative relevance prediction was obtained from collaborative filtering. 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.