Network-Aware Recommender System via Online Feedback Optimization

Personalized content on social platforms can exacerbate negative phenomena such as polarization, partly due to the feedback interactions between users and recommendations. In this paper, we present a control-theoretic recommender system that explicitly accounts for this feedback loop to mitigate polarization. Our approach extends <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online feedback optimization</i> – a control paradigm for steady-state optimization of dynamical systems – to develop a recommender system that provides a trade-off 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 Friedkin0-Johnsen model. We showcase that “network-aware” recommendation significantly reduces polarization while maintaining high levels of user engagement.