Inferring Users' Critiquing Feedback on Recommendations from Eye Movements

In recommender systems, critiquing has been popularly applied as an effective approach to obtaining users’ feedback on recommended products. In order to reduce users’ efforts of creating critiquing criteria on their own, some systems have aimed at suggesting critiques for users to choose. How to accurately match system-suggested critiques to users’ intended feedback hence becomes a challenging issue. In this paper, we particularly take into account users’ eye movements on recommendations to infer their critiquing feedback. Based on a collection of real users’ eye-gaze data, we have demonstrated the approach’s feasibility of implicitly deriving users’ critiquing criteria. It hence indicates a promising direction of using eye-tracking technique to improve existing critique suggestion methods.