Stereotype-aware collaborative filtering

Abstract

In collaborative filtering, recommendations are made using user feedback on a few products. In this paper, we show that even if sensitive attributes are not used to fit the models, a disparate impact may nevertheless affect recommendations. We propose a definition of fairness for the recommender system that expresses that the ranking of items should be independent of sensitive attribute. We design a co- clustering of users and items that processes exogenous sensitive attributes to remove their influence to return fair recommendations. We prove that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.