用强化学习实现健身计划全要素推荐，提升用户参与度。
Personalized Recommender Systems for Gym Workouts: A Reinforcement Learning Approach
- 基于强化学习构建四类环境，综合推荐动作、组数、次数与负荷。
- 合成用户实验显示，完整处方比仅推荐动作提升奖励与参与度。
- 可捕捉用户跳过行为，实现动态个性化，适合真实健身场景。
健身推荐系统旨在帮助用户完成高效且有趣的训练。然而，仅推荐动作不足以满足实际需求，一个实用系统还需确定合适的组数、重复次数和训练负荷，并适应用户跳过动作等行为。现有方法通常只考虑部分因素，限制了其在真实场景的应用。本文将健身推荐从动作选择扩展到完整训练计划制定，提出一种基于强化学习的框架，包含四种环境：仅动作推荐与完整处方设置，每种均含是否支持跳过行为的版本。完整处方环境同时推荐动作、组数、重复次数与负荷；跳过支持环境利用用户跳过行为进行在线个性化。合成用户实验表明，建模完整处方任务能带来更高奖励与更强用户参与度，凸显真实训练规划在个性化健身推荐中的重要性。
原文摘要 · Abstract (English)
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.
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