Personalized Content Recommender System via Non-verbal Interaction Using Face Mesh and Facial Expression

Yuya Moroto, Rintaro Yanagi, Naoki Ogawa, Kyohei Kamikawa, Keigo Sakurai, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

Abstract: Multimedia content recommendation needs to consider users' preferences for each content. Conventional recommender systems consider them with wearable sensors, however, wearing such sensors can lead to a burden on users. In this paper, we construct a recommender system that can explicitly estimate users' preferences without wearable sensors. Specifically, by constructing lightweight but strong machine learning models suitable for our system, the users' interest levels for contents can be estimated from facial images obtained from a widely used webcam. In addition, through the interaction that the user selects displayed contents, our system finds the tendency of personal preferences for recommending contents with high user satisfaction. Our system is available on https://www.lmd-demo.org/2022/start_eng.html.