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Crank up the volume: preference bias amplification in collaborative recommendation

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arxiv 1909.06362 v1 pith:7UVYOZHR submitted 2019-09-13 cs.IR cs.LGstat.ML

Kun Lin , Nasim Sonboli , Bamshad Mobasher , Robin Burke This is my paper · ORCID
classification cs.IRcs.LGstat.ML
keywords biasdifferentuseralgorithmsdisparitypreferencesaffectsamplification
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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' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine bias disparity over a range of different algorithms and for different item categories and demonstrate significant differences between model-based and memory-based algorithms.

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  1. Datasets for Navigating Sensitive Topics in Recommendation Systems

    cs.IR 2025-09 conditional novelty 5.0 of 10

    Two benchmark datasets link user-preference data (MovieLens, Archive of Our Own) with community content-warning labels to study sensitive-content exposure in recommender systems.

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