Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
Information Retrieval
2019-08-05 v1 Machine Learning
Social and Information Networks
Abstract
Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.
Cite
@article{arxiv.1908.00831,
title = {Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison},
author = {Masoud Mansoury and Bamshad Mobasher and Robin Burke and Mykola Pechenizkiy},
journal= {arXiv preprint arXiv:1908.00831},
year = {2019}
}
Comments
Workshop on Recommendation in Multi-Stakeholder Environments (RMSE) at ACM RecSys 2019, Copenhagen, Denmark