  There has been growing attention on fairness considerations recently,
especially in the context of intelligent decision making systems. Explainable
recommendation systems, in particular, may suffer from both explanation bias
and performance disparity. In this paper, we analyze different groups of users
according to their level of activity, and find that bias exists in
recommendation performance between different groups. We show that inactive
users may be more susceptible to receiving unsatisfactory recommendations, due
to insufficient training data for the inactive users, and that their
recommendations may be biased by the training records of more active users, due
to the nature of collaborative filtering, which leads to an unfair treatment by
the system. We propose a fairness constrained approach via heuristic re-ranking
to mitigate this unfairness problem in the context of explainable
recommendation over knowledge graphs. We experiment on several real-world
datasets with state-of-the-art knowledge graph-based explainable recommendation
algorithms. The promising results show that our algorithm is not only able to
provide high-quality explainable recommendations, but also reduces the
recommendation unfairness in several respects.
