Review-Based Explainable Recommendations: A Transparency Perspective

With the prevalence of deep learning–based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, it is difficult to determine which aspects of the deep models’ input drive the final ranking decision in many of these recommender systems; thus, they often cannot be understood by human stakeholders. In this article, 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 changes in 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 datasets under a contextualized setting in which the users provided explicit reviews of the items. The empirical results show that our models can achieve highly competitive ranking performance while generating accurate and effective explanations in terms of numerous quantitative metrics and qualitative visualizations.