User Fairness in Recommender Systems
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Companion Proceedings of the The Web Conference 2018, page 101--102. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2018)

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.
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