Classifeye: Classification of Personal Characteristics Based on Eye Tracking Data in a Recommender System Interface.
Martijn Millecamp, Cristina Conati, Katrien Verbert · Lirias · 2021
Due to the increasing importance of recommender systems in our life, the call to make these systems more transparent becomes louder. However, providing explanations is not as easy as it seems, as research has shown that different users have varying reactions to explanations. So not only the recommendations, but also the explanations should be personalised. As a first step towards these personalised explanations, we explore the possibility to classify users based on their gaze pattern during the interaction with a music recommender system. More specifically, we classify three personal characteristics that have been shown to play a role in the interaction with music recommendations: need for cognition, openness and musical sophistication. Our results show that classification based on eye tracking has potential for need for cognition and openness, as we are able to do better than random, but not for musical sophistication as no classifier did better than a uniform random baseline.