  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.
