  Knowledge graphs (KG) have become increasingly important to endow modern
recommender systems with the ability to generate traceable reasoning paths to
explain the recommendation process. However, prior research rarely considers
the faithfulness of the derived explanations to justify the decision making
process. To the best of our knowledge, this is the first work that models and
evaluates faithfully explainable recommendation under the framework of KG
reasoning. Specifically, we propose neural logic reasoning for explainable
recommendation (LOGER) by drawing on interpretable logical rules to guide the
path reasoning process for explanation generation. We experiment on three
large-scale datasets in the e-commerce domain, demonstrating the effectiveness
of our method in delivering high-quality recommendations as well as
ascertaining the faithfulness of the derived explanation.
