AI 中文总结
该研究针对健身房锻炼推荐问题，提出一种强化学习框架，将推荐扩展至完整锻炼方案制定，通过实验验证完整方案建模可提升奖励与用户参与度。
AI 中文摘要
锻炼推荐系统旨在帮助健身房用户完成高效且有趣的训练课程。然而，仅推荐锻炼项目是不够的，实际系统还必须确定合适的组数、重复次数和训练负荷，同时适应用户跳过锻炼等行为。现有方法通常仅考虑这些因素的子集，限制了其在实际场景中的适用性。在本文中，我们将锻炼推荐从锻炼项目选择扩展到完整锻炼方案制定。我们提出了一种基于强化学习（RL）的框架，包含四种环境：仅锻炼项目设置、完整方案设置，每种设置又分为支持用户跳过交互和不支持用户跳过交互两类。完整方案环境会推荐锻炼项目、组数、重复次数和负荷，而支持跳过的环境会利用用户跳过行为进行在线个性化调整。针对合成用户的实验表明，与仅推荐锻炼项目相比，对完整方案任务进行建模能带来更高的奖励和更强的用户参与度，凸显了现实锻炼规划在个性化健身房推荐系统中的重要性。
英文摘要
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.
