  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.
