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Co-clustering for fair recommendation

Abstract : 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.
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Contributor : Gabriel Frisch Connect in order to contact the contributor
Submitted on : Thursday, May 27, 2021 - 4:56:07 PM
Last modification on : Thursday, November 18, 2021 - 3:44:53 PM
Long-term archiving on: : Saturday, August 28, 2021 - 8:03:14 PM


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  • HAL Id : hal-03239856, version 1



Gabriel Frisch, Jean-Benoist Leger, Yves Grandvalet. Co-clustering for fair recommendation. 2021. ⟨hal-03239856v1⟩



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