  With the prevalence of deep learning based embedding approaches, recommender
systems have become a proven and indispensable tool in various information
filtering applications. However, many of them remain difficult to diagnose what
aspects of the deep models' input drive the final ranking decision, thus, they
cannot often be understood by human stakeholders. In this paper, we investigate
the dilemma between recommendation and explainability, and show that by
utilizing the contextual features (e.g., item reviews from users), we can
design a series of explainable recommender systems without sacrificing their
performance. In particular, we propose three types of explainable
recommendation strategies with gradual change of model transparency: whitebox,
graybox, and blackbox. Each strategy explains its ranking decisions via
different mechanisms: attention weights, adversarial perturbations, and
counterfactual perturbations. We apply these explainable models on five
real-world data sets under the contextualized setting where users and items
have explicit interactions. The empirical results show that our model achieves
highly competitive ranking performance, and generates accurate and effective
explanations in terms of numerous quantitative metrics and qualitative
visualizations.
