Co-clustering for fair recommendation

Gabriel Frisch (1) , Jean-Benoist Leger (1) , Yves Grandvalet (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

Collaborative filtering relies on a sparse rating matrix, where each user rates a few products, to propose recommendations. The approach consists to approximate 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 ensure fair recommendations. Since users are only characterized by their ratings and their sensitive attribute, fairness is measured here by a parity criterion. Introducing the sensitive attribute in the latent block model leads to a classification of users that is independent from the sensitive attribute. We propose a definition of fairness for the recommender system that expresses that the ranking of items should be independent of the sensitive attribute. We show that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]

Gabriel Frisch, Jean-Benoist Leger, Yves Grandvalet. Co-clustering for fair recommendation. 21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), Sep 2021, Bilbao, Spain. pp.607-630, ⟨10.1007/978-3-030-93736-2_44⟩. ⟨hal-03239856v1⟩