Network-aware Recommender System via Online Feedback Optimization

TL;DR

This paper introduces a control-theoretic recommender system that uses online feedback optimization to reduce polarization on social platforms while maintaining user engagement, based solely on click data.

Contribution

It extends online feedback optimization to develop a network-aware recommender system that balances engagement and polarization reduction with theoretical guarantees.

Findings

Significantly reduces polarization in simulated social networks.

Maintains high user engagement levels.

Provides theoretical guarantees for stability and optimality.

Abstract

Personalized content on social platforms can exacerbate negative phenomena such as polarization, partly due to the feedback interactions between recommendations and the users. In this paper, 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 trades off users engagement and polarization reduction, 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 Friedkin-Johnsen dynamics. Our results show these "network-aware" recommendations can significantly reduce polarization while maintaining high levels of user engagement.