  Much like other learning-based models, recommender systems can be affected by
biases in the training data. While typical evaluation metrics (e.g. hit rate)
are not concerned with them, some categories of final users are heavily
affected by these biases. In this work, we propose using multiple triplet
losses terms to extract meaningful and robust representations of users and
items. We empirically evaluate the soundness of such representations through
several "bias-aware" evaluation metrics, as well as in terms of stability to
changes in the training set and agreement of the predictions variance w.r.t.
that of each user.
