Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison

2019-08-02 · Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.

📄 PDF Abstract BibTeX arXiv:1908.00831

Code (1)

masoudmansoury/yelp_core40 공식 구현

Tasks

BIG-bench Machine Learning Fairness Recommendation Systems

Crank up the volume: preference bias amplification in collaborative recommendation

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Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' st…

Recommendation Systems

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Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' …

Collaborative Filtering Music Recommendation Recommendation Systems

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2026-07-15 · Jiwen Zhou, Xiang Liu, Mingming Li, Pengbo Mo 외 arxiv

Recommender systems operate as Black-Boxes, leaving users and regulators unable to steer their outputs toward specific intentions or audit their behavior. This lack of controllability, defined as the system's ability to …

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2020-02-04 · Sunshine Chong, Andrés Abeliuk

Recommendation systems today exert a strong influence on consumer behavior and individual perceptions of the world. By using collaborative filtering (CF) methods to create recommendations, it generates a continuous feedb…

Collaborative Filtering Recommendation Systems

Emergent Instabilities in Algorithmic Feedback Loops

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Algorithms that aid human tasks, such as recommendation systems, are ubiquitous. They appear in everything from social media to streaming videos to online shopping. However, the feedback loop between people and algorithm…