Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards increased consumption risks burn-out, churn, or even addictive habits. To promote digital well-being, most platforms now offer a service that periodically prompts users to take breaks. These, however, must be set up manually, and so may be suboptimal for both users and the system. In this paper, we study the role of breaks in recommendation, and propose a framework for learning optimal breaking policies that promote and sustain long-term engagement. Based on the notion that recommendation dynamics are susceptible to both positive and negative feedback, we cast recommendation as a Lotka-Volterra dynamical system, where breaking reduces to a problem of optimal control. We then give an efficient learning algorithm, provide theoretical guarantees, and empirically demonstrate the utility of our approach on semi-synthetic data.

翻译：优化用户参与度是现代推荐系统的关键目标，但盲目推动用户增加消费可能导致倦怠、流失甚至成瘾行为。为促进数字福祉，大多数平台现提供定期提示用户休息的服务。然而，这些提示需手动设置，因此可能对用户和系统均非最优。本文研究了休息在推荐中的作用，并提出一个框架，用于学习能够促进并维持长期参与的最优休息策略。基于推荐动态易受正负反馈影响的观点，我们将推荐建模为Lotka-Volterra动力系统，其中休息简化为最优控制问题。随后，我们给出一种高效的学习算法，提供理论保证，并基于半合成数据实证展示了该方法的效用。