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User Fairness in Recommender Systems by Jurek Leonhardt, Avishek Anand, and Megha Khosla is a scholarly article available to read on EtoBox.

What is User Fairness in Recommender Systems about?

Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.

Author

Jurek Leonhardt, Avishek Anand, and Megha Khosla

Publisher

ACM Press

Published

2018

Language

EN