Crank up the volume: preference bias amplification in collaborative recommendation
Information Retrieval
2019-09-17 v1 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1909.06362,
title = {Crank up the volume: preference bias amplification in collaborative recommendation},
author = {Kun Lin and Nasim Sonboli and Bamshad Mobasher and Robin Burke},
journal= {arXiv preprint arXiv:1909.06362},
year = {2019}
}
Comments
Presented at the RMSE workshop held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), 2019, in Copenhagen, Denmark