Learning to Take a Break: Sustainable Optimization of Long-Term User Engagement

Eden Saig, Nir Rosenfeld

Keywords: Lotka-Volterra dynamics, breaking policies, digital well-being, feed-based recommendation

TL;DR: We use Lotka-Volterra dynamics to learn optimal `take-a-break' schedules that promote sustainable media habits.

Abstract:

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 a break. These, however, must be set up manually, and so may be suboptimal for both users and the system.
In this paper, we propose a framework for optimizing long-term engagement by learning individualized breaking policies. Using Lotka-Volterra dynamics, we model users as acting based on two balancing latent states: drive, and interest---which must be conserved. We then give an efficient learning algorithm, provide theoretical guarantees, and empirically evaluate its performance on semi-synthetic data.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2211.13585/code)