Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
This addresses the challenge of sustainable user engagement for recommendation platforms, offering a novel approach to break scheduling, though it is incremental as it builds on existing break services and control theory.
The paper tackles the problem of optimizing long-term user engagement in recommendation systems by learning to suggest breaks, aiming to prevent burn-out and churn while promoting digital well-being. It proposes a framework based on Lotka-Volterra dynamics, provides theoretical guarantees and an efficient algorithm, and demonstrates utility on semi-synthetic data.
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.