Wearable devices make self-monitoring easier by the users, who usually tend to increase physical activity and weight loss maintenance over time. But in terms of behavior adaptation to these goals, these devices do not provide specific features beyond monitoring the achievement of daily goals, such as a number of steps or miles walked and caloric outtake. The purpose of this study is twofold. By analyzing a large dataset of signals collected by these devices, we identify significant clusters of similar behavior patterns related to user physical activities. We then examine specific patterns of step count in the context of recommendation of habits that more likely give rise to weight loss effects. The evaluation of the effectiveness of these personalized recommendations, based on a comparative study, proves how a recommender system based on the reinforcement learning paradigm is able to guarantee better performance for this task by balancing the trade-off between long-term and short-term rewards.

中文翻译：

---

通过挖掘活动跟踪器数据制定个性化减肥策略

可穿戴设备使用户更容易进行自我监控，随着时间的推移，他们通常倾向于增加身体活动和减肥维持。但就对这些目标的行为适应而言，这些设备除了监测日常目标的实现之外，并没有提供特定的功能，例如步行的步数或英里数和卡路里摄入量。这项研究的目的是双重的。通过分析这些设备收集的大量信号数据集，我们确定了与用户身体活动相关的类似行为模式的重要集群。然后，我们在推荐更可能产生减肥效果的习惯的背景下检查步数的特定模式。基于比较研究评估这些个性化建议的有效性，