Harm Mitigation in Recommender Systems under User Preference Dynamics | Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024)

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Authors: Jerry Chee, Shankar Kalyanaraman, Sindhu Kiranmai Ernala, Udi Weinsberg, Sarah Dean, Stratis Ioannidis

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 255 - 265

Published: 24 August 2024 Publication History

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Abstract

Harm Mitigation in Recommender Systems under User Preference Dynamics | Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (7)

We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish conditions under which the user profile dynamics have a stationary point, and propose algorithms for finding an optimal recommendation policy at stationarity. We experiment on a semi-synthetic movie recommendation setting initialized with real data and observe that our policies outperform baselines at simultaneously maximizing CTR and mitigating harm.

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Cited By

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  • Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024

    https://dl.acm.org/doi/10.1145/3637528.3671925

Index Terms

  1. Harm Mitigation in Recommender Systems under User Preference Dynamics

    1. Human-centered computing

      1. Collaborative and social computing

        1. Collaborative and social computing theory, concepts and paradigms

          1. Social media

      2. Information systems

        1. Information retrieval

          1. Retrieval tasks and goals

            1. Recommender systems

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      Harm Mitigation in Recommender Systems under User Preference Dynamics | Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (8)

      KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

      August 2024

      6901 pages

      ISBN:9798400704901

      DOI:10.1145/3637528

      • General Chairs:
      • Ricardo Baeza-Yates

        Northeastern University, USA

        ,
      • Francesco Bonchi

        CENTAI / Eurecat, Italy

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      Published: 24 August 2024

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      Author Tags

      1. amplification
      2. harm mitigation
      3. recommender systems
      4. user preference modeling

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      Harm Mitigation in Recommender Systems under User Preference Dynamics | Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (11)

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      • Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024

        https://dl.acm.org/doi/10.1145/3637528.3671925

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