Personalizing Exercise Recommendations with Explanations using Multi-Armed Contextual Bandit and Reinforcement Learning

Abstract

We present an innovative mobile exercise recommendation app that leverages clinical guidelines from authoritative sources to provide personalized, safe exercise suggestions. Our approach addresses two critical challenges in health-focused recommender systems: the cold start problem and user motivation through explainable AI. To overcome the initial lack of user data, we employ a two-stage process: We use Deep Q-Network (DQN) reinforcement learning to generate 2000 synthetic user profile. The DQN learns a reward function based on clinical guidelines, ensuring that the generated profiles align with established medical advice. These synthetic profiles bootstrap a multi-armed contextual bandit algorithm. This algorithm recommends the most suitable exercises for a given user persona, determined by a combination of comorbidities, age, and preferred exercise criteria. Our method’s key innovation lies in its ability to mimic a large cohort of clinically safe user profiles without requiring real-world participants, effectively eliminating the cold start problem while maintaining medical appropriateness. To enhance user engagement and promote behavior change, we implement an explainability layer. Unlike black-box deep learning recommenders, our system provides transparent justifications for each recommendation. By highlighting the importance of specific features used in the decision-making process, we help users understand why a particular exercise is recommended for their persona. This recommender system is being incorporated into an existing mobile app, which will be trialed with healthy and cardiovascular disease patients.