  Eye tracking in recommender systems can provide an additional source of
implicit feedback, while helping to evaluate other sources of feedback. In this
study, we use eye tracking data to inform a collaborative filtering model for
movie recommendation providing an improvement over the click-based
implementations and additionally analyze the area of interest (AOI) duration as
related to the known information of click data and movies seen previously,
showing AOI information consistently coincides with these items of interest.
