User Fairness in Recommender Systems
This addresses fairness issues for users in recommender systems, highlighting an overlooked aspect that could impact user experience and equity.
The paper tackles the problem of user discrimination caused by diversity-focused post-processing algorithms in recommender systems, showing that increased aggregate diversity leads to greater disparity among users.
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.