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October 15, 2025Open Access

Looking for Fairness in Recommender Systems

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Authors

CLCécile Logé

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Implication

This framework evaluates recommender systems' fairness and suggests metrics to improve diversity, highlighting the role of algorithmic recommendations.

Key Points

  • Addressing fairness in recommender systems aims to prevent filter bubbles, enhancing user experience and societal outcomes.
  • The creation of filter bubbles can manipulate users, hinder content creators, and influence broader social behaviors.
  • Incorporating diversity metrics into recommender systems can balance personalized recommendations with diverse cultural perspectives.
  • Evaluating algorithmic recommendations is crucial for ensuring a varied content landscape that avoids societal polarization.

Cite This Study

Cécile Logé (2025) studied this question.

synapsesocial.com/papers/68ef858cc6a308ba063556a4https://doi.org/10.48550/arxiv.2507.12242
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  1. 1Promoting Fairness in Recommender Systems: A Multifaceted Approach2024
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  3. 3Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation2024 · 20 citations
  4. 4Beyond Trade-offs: Unveiling Fairness-Constrained Diversity in News Recommender Systems2024 · 10 citations
  5. 5Transparency, Privacy, and Fairness in Recommender Systems2024