DeepFair: Deep Learning for Improving Fairness in Recommender Systems

• URL: http://arxiv.org/abs/2006.05255v1
• Date: Tue, 9 Jun 2020 13:39:38 GMT
• Title: DeepFair: Deep Learning for Improving Fairness in Recommender Systems
• Authors: Jes\'us Bobadilla, Ra\'ul Lara-Cabrera, \'Angel Gonz\'alez-Prieto, Fernando Ortega
• Abstract summary: The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. We propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users.
• Score: 63.732639864601914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy.

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