Crank up the volume: preference bias amplification in collaborative recommendation
This work addresses bias amplification in recommender systems, which is an incremental analysis of existing calibration and disparity issues.
The paper examined bias disparity across various recommendation algorithms and item categories, finding significant differences between model-based and memory-based approaches.
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.