  Recent work in recommender systems has emphasized the importance of fairness,
with a particular interest in bias and transparency, in addition to predictive
accuracy. In this paper, we focus on the state of the art pairwise ranking
model, Bayesian Personalized Ranking (BPR), which has previously been found to
outperform pointwise models in predictive accuracy, while also being able to
handle implicit feedback. Specifically, we address two limitations of BPR: (1)
BPR is a black box model that does not explain its outputs, thus limiting the
user's trust in the recommendations, and the analyst's ability to scrutinize a
model's outputs; and (2) BPR is vulnerable to exposure bias due to the data
being Missing Not At Random (MNAR). This exposure bias usually translates into
an unfairness against the least popular items because they risk being
under-exposed by the recommender system. In this work, we first propose a novel
explainable loss function and a corresponding Matrix Factorization-based model
called Explainable Bayesian Personalized Ranking (EBPR) that generates
recommendations along with item-based explanations. Then, we theoretically
quantify additional exposure bias resulting from the explainability, and use it
as a basis to propose an unbiased estimator for the ideal EBPR loss. The result
is a ranking model that aptly captures both debiased and explainable user
preferences. Finally, we perform an empirical study on three real-world
datasets that demonstrate the advantages of our proposed models.
