Crank up the volume: preference bias amplification in collaborative recommendation

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.

arXiv commentPresented at the RMSE workshop held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), 2019, in Copenhagen, Denmark

References in corpus (4)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Equality of Opportunity in Supervised Learning
• Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
• Bias Disparity in Recommendation Systems

Cited by in corpus (2)

• Unbiased Knowledge Distillation for Recommendation
• Debiasing Learning for Membership Inference Attacks Against Recommender Systems