Network-aware Recommender System via Online Feedback Optimization
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929515903582208 |
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| author | Chandrasekaran, Sanjay De Pasquale, Giulia Belgioioso, Giuseppe Dörfler, Florian |
| author_facet | Chandrasekaran, Sanjay De Pasquale, Giulia Belgioioso, Giuseppe Dörfler, Florian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Network-aware Recommender System via Online Feedback Optimization Chandrasekaran, Sanjay De Pasquale, Giulia Belgioioso, Giuseppe Dörfler, Florian Systems and Control Optimization and Control 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. |
| title | Network-aware Recommender System via Online Feedback Optimization |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2408.16899 |