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.
Crank up the volume: preference bias amplification in collaborative recommendation
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abstract
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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Datasets for Navigating Sensitive Topics in Recommendation Systems
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.