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

Eden Saig, Nir Rosenfeld
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:29671-29696, 2023.

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 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-saig23a, title = {Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement}, author = {Saig, Eden and Rosenfeld, Nir}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {29671--29696}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/saig23a/saig23a.pdf}, url = {https://proceedings.mlr.press/v202/saig23a.html}, 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 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.} }
Endnote
%0 Conference Paper %T Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement %A Eden Saig %A Nir Rosenfeld %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-saig23a %I PMLR %P 29671--29696 %U https://proceedings.mlr.press/v202/saig23a.html %V 202 %X 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.
APA
Saig, E. & Rosenfeld, N.. (2023). Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:29671-29696 Available from https://proceedings.mlr.press/v202/saig23a.html.

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