Co-clustering for Fair Recommendation

Gabriel Frisch, Jean-Benoist Léger, Yves Grandvalet

Abstract: Collaborative filtering relies on a sparse rating matrix, where each user rates a few products, to propose recommendations. The approach consists of approximating the sparse rating matrix with a simple model whose regularities allow to fill in the missing entries. The latent block model is a generative co-clustering model that can provide such an approximation. In this paper, we show that exogenous sensitive attributes can be incorporated in this model to make fair recommendations. Since users are only characterized by their ratings and their sensitive attribute, fairness is measured here by a parity criterion. We propose a definition of fairness specific to recommender systems, requiring item rankings to be independent of the users’ sensitive attribute. We show that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.