تقرير
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
| العنوان: | Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement |
|---|---|
| المؤلفون: | Saig, Eden, Rosenfeld, Nir |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computers and Society, Computer Science - Information Retrieval, Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | 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. Comment: Accepted for publication in ICML 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.13585 |
| رقم الأكسشن: | edsarx.2211.13585 |
| قاعدة البيانات: | arXiv |
كن أول من يترك تعليقا!