Personalized Recommender Systems for Gym Workouts: A Reinforcement Learning Approach
Information Retrieval
Summary
The authors study how to recommend not just exercises, but also the number of sets, repetitions, and weights for gym workouts. They use a method called reinforcement learning to create systems that adapt if users skip exercises. Their tests with simulated users show that recommending full workout plans leads to better results and keeps users more engaged than just suggesting exercises. This means considering detailed workout details is important for making good gym recommendations.
Authors
Roan Rosema, Helma Torkamaan, Masoud Mansoury
Abstract
Workout recommender systems aim to help gym users complete effective and engaging training sessions. However, recommending exercises alone is insufficient, as a practical system must also determine appropriate sets, repetitions, and training loads, while adapting to user behavior such as skipping exercises. Existing approaches typically consider only a subset of these factors, limiting their applicability in real-world settings. In this paper, we extend workout recommendation from exercise selection to full workout prescription. We propose a reinforcement learning (RL)-based framework with four environments: exercise-only and full-prescription settings, each with and without skip-based interaction. The full-prescription environments recommend exercises, sets, repetitions, and load, while the skip-enabled environments use user skipping behavior for online personalization. Experiments with synthetic users show that modeling the full prescription task leads to higher rewards and greater user engagement than exercise-only recommendation, highlighting the importance of realistic workout planning in personalized gym recommender systems.