Work

Network-Aware Recommender System via Online Feedback Optimization

Year: 2025

Type: article

Abstract: 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 extendsonline feedback optimization– 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.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2025.3616262

Authors: Sanjay Chandrasekaran, Giulia De Pasquale, Giuseppe Belgioioso, Florian Dörfler

Institutions: ETH Zurich, Automatic Control Laboratory, Eindhoven University of Technology, KTH Royal Institute of Technology

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Knut och Alice Wallenbergs Stiftelse

OpenAlex ID: W7083808182

FWCI: 1.083

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Cited by: 1

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