# Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement

arXiv: https://arxiv.org/abs/2211.13585 · HTML: https://mlsanity.com/paper/2211.13585/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [Fine-Grained Spoiler Detection from Large-Scale Review Corpora](https://mlsanity.com/paper/1905.13416/) (2019)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization](https://mlsanity.com/paper/2202.11776/) (2022)
- [Black-box Variational Inference for Stochastic Differential Equations](https://mlsanity.com/paper/1802.03335/) (2018)
- [Do Offline Metrics Predict Online Performance in Recommender Systems?](https://mlsanity.com/paper/2011.07931/) (2020)
- [Preference Dynamics Under Personalized Recommendations](https://mlsanity.com/paper/2205.13026/) (2022)
- [Rebounding Bandits for Modeling Satiation Effects](https://mlsanity.com/paper/2011.06741/) (2020)
- [Human Interaction with Recommendation Systems](https://mlsanity.com/paper/1703.00535/) (2017)
- [Scalable Variational Inference for Dynamical Systems](https://mlsanity.com/paper/1705.07079/) (2017)
- [Fatigue-aware Bandits for Dependent Click Models](https://mlsanity.com/paper/2008.09733/) (2020)
- [The Stereotyping Problem in Collaboratively Filtered Recommender Systems](https://mlsanity.com/paper/2106.12622/) (2021)
- [Towards Psychologically-Grounded Dynamic Preference Models](https://mlsanity.com/paper/2208.01534/) (2022)
- [The turnpike property in finite-dimensional nonlinear optimal control](https://mlsanity.com/paper/1402.3263/) (2014)
- [Recommendation System Simulations: A Discussion of Two Key Challenges](https://mlsanity.com/paper/2109.02475/) (2021)

## Built on by (most cited first, 3 of 3)

- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Brainrot: Deskilling and Addiction are Overlooked AI Risks](https://mlsanity.com/paper/2605.03512/) (2026)
- [The Burden of Interactive Alignment with Inconsistent Preferences](https://mlsanity.com/paper/2510.16368/) (2025)

## Abstract (arXiv)

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.