Reinforcement Learning Architecture for Facial Skin Treatment Recommender

Abstract

In the domain of facial skin treatment, personalization of treatment methods and management of treatment effectiveness among users/patients become the key challenges. Our approach to addressing the challenges is to develop a treatment recommender by utilizing a policy of reinforcement learning and continuously optimizing the policy to learn the variability. We leverage algorithmic decision-making through the Reinforcement Learning (RL) model's policy to personalize recommendations according to the unique characteristics and treatment responses of each user. This RL-based recommender should provide a high performance of personalizing treatment recommendations and continue to learn user-specific effectiveness of treatment methods. Preliminary results demonstrate the system's capacity to provide targeted, effective skin care recommendations, significantly enhancing user satisfaction and adherence to treatments.