A bias detection tree approach for detecting disparities in a recommendation model’s errors

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Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

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Sampling-bias-corrected neural modeling for large corpus item recommendations

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Reducing disparate exposure in ranking: A learning to rank approach

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Connecting user and item perspectives in popularity debiasing for collaborative recommendation

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Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

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Popularity Bias in Dynamic Recommendation

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Addressing Marketing Bias in Product Recommendations

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The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses

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Beyond Globally Optimal: Focused Learning for Improved Recommendations

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Local Popularity and Time in top-N Recommendation

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Power of the Few

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Attribute-based evaluation for recommender systems

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Fairness in Reviewer Recommendations at Elsevier

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When Is a Recommendation Model Wrong? A Model-Agnostic Tree-Based Approach to Detecting Biases in Recommendations

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A Comparative Study of Fairness in Medical Machine Learning

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“Guess Why I didn't rate it”: A New Preference-based Model for Enhanced TopK Recommendation

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Integrating Social Environment in Machine Learning Model for Debiased Recommendation

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Designing Fair LLM-Based Matching Systems: A Multi-level Framework for Bias Identification and Mitigation

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A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research

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When Is a Recommendation Model Wrong? A Model-Agnostic Tree-Based Approach to Detecting Biases in Recommendations

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Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems

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FaiRecSys: mitigating algorithmic bias in recommender systems

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Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison

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When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation

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