论文标题
学习建议休息：长期用户参与的可持续优化
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
论文作者
论文摘要
优化用户参与是现代推荐系统的关键目标，但盲目地推动用户提高消费风险燃烧，流失甚至上瘾的习惯。为了促进数字福祉，大多数平台现在提供一项服务，该服务会定期提示用户休息。但是，必须手动设置这些设置，因此对于用户和系统而言，可能是次优的。在本文中，我们研究了中断在推荐中的作用，并提出了一个学习最佳破坏政策的框架，以促进和维持长期参与。基于建议动力学易受正反馈和负面反馈的概念，我们将建议作为Lotka-Volterra动力学系统，其中破裂减少到最佳控制问题。然后，我们提供有效的学习算法，提供理论保证，并从经验上证明我们在半合成数据上的方法。
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.
