Network-Aware Recommender System via Online Feedback Optimization

doi:10.1109/TAC.2025.3616262

Network-Aware Recommender System via Online Feedback Optimization

2025-09-30

0

PRE

AI

S

Sanjay Chandrasekaran

DOI：10.1109/TAC.2025.3616262

Original

Original request for help

Save

Abstract

En 中文

Personalized content on social platforms can exacerbate negative phenomena, such as polarization, partly due to the feedback interactions between users and recommendations. In this article, we present a control-theoretic recommender system that explicitly accounts for this feedback loop to mitigate polarization. Our approach extends online feedback optimization—a control paradigm for steady-state optimization of dynamical systems—to develop a recommender system that provides a tradeoff between user engagement and polarization mitigation, while relying solely on online click data. We establish theoretical guarantees for optimality and stability of the proposed design and validate its effectiveness via numerical experiments with a user population governed by the Friedkin–Johnsen model. We showcase that “network-aware” recommendation significantly reduces polarization while maintaining high levels of user engagement.

Keywords:

Networked control systems

nonconvex optimization

opinion dynamics

recommender systems

Network-Aware Recommender System via Online Feedback Optimization

Abstract

Journal

Organization