Bias in Recommender Systems: Item Price Perspective
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Investigating the impact of recommender systems on user-based and item-based popularity bias
AbstractRecommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they ...
Highlights- We measure reinforced recommendation-driven popularity bias on several algorithms.
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 ...
Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender Systems
WWW '24: Companion Proceedings of the ACM Web Conference 2024Recommender systems are essential for finding personalized content for users on online platforms. These systems are often trained on historical user interaction data, which collects user feedback on system recommendations. This creates a feedback loop ...
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Berlin, Heidelberg
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