Acquiring Unobtrusive Relevance Feedback through Eye-Tracking in Ambient Recommender Systems

Authors:
Gustavo González;Beatriz López;Cecilio Angulo;Josep Lluís de la Rosa
Affiliations:
University of Girona. Institute of Informatics and Applications. Agents Research Lab.;University of Girona. Institute of Informatics and Applications. Agents Research Lab.;Technical University of Catalonia. Knowledge Engineering Research Group;University of Girona. Institute of Informatics and Applications. Agents Research Lab.
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

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Abstract

Acquiring relevant information to keep user's preferences up-to-date is crucial in recommender systems in order to close the cycle of recommendations. Ambient Intelligence is a suitable approach for non-intrusively closing the loop in recommender systems using ambient eye-trackers. We combine a method for acquiring relevance feedback through eye-tracking with the functionalities of an extractor agent. We describe the results of experiments conducted in a recommender system to obtain implicit feedback using eye fixations. Finally, we obtain a ranking of user's most relevant preferences and behaviours.