A Two-Stage Calibration Approach for Mitigating Bias and Fairness in Recommender Systems
Pages 1659 - 1661
Abstract
Popularity bias and unfairness are problems caused by the lack of calibration in recommender systems. Works that intend to reduce the effect of popularity bias do not consider the distribution of item genres/categories in the users' profiles. Other studies aim to calibrate the system to generate fair recommendations according to users' profiles, but usually are still biased towards popularity. We propose a system calibration approach based on users' preferences for different levels of popularity of items and their genres. The proposed approach works in the post-processing stage and can be combined with different recommendation models. We evaluated the system with offline experiments using one state-of-the-art dataset, three recommender algorithms, six baselines, and different metrics for popularity, fairness, and accuracy. The results indicate reduced popularity bias and improved fairness.
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Index Terms
- A Two-Stage Calibration Approach for Mitigating Bias and Fairness in Recommender Systems
Recommendations
User-centered Evaluation of Popularity Bias in Recommender Systems
UMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and PersonalizationRecommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for mitigating ...
Long-tail session-based recommendation from calibration
AbstractAccurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias. Existing models for mitigating popularity bias have attempted to reduce the ...
EqBal-RS: Mitigating popularity bias in recommender systems
AbstractRecommender systems are deployed heavily by many online platforms for better user engagement and providing recommendations. Despite being so popular, several works have shown the existence of popularity bias due to the non-random nature of missing ...
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April 2024
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Published: 21 May 2024
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