Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.

翻译：为推荐结果提供系统生成的解释，是实现透明可信推荐系统的重要一步。可解释推荐系统能够为其输出提供人类可理解的依据。过去二十年间，可解释推荐已成为推荐系统研究领域备受关注的方向。本文旨在全面回顾推荐系统中可视化解释的相关研究工作。具体而言，我们从解释目标、解释范围、解释风格和解释格式四个维度，系统梳理了推荐系统解释的相关文献。鉴于可视化的重要性，我们以解释性可视化（即采用可视化作为解释的展示风格）为切入点审视推荐系统文献。据此，我们提炼出一套可用于设计推荐系统解释性可视化的指导原则，并指出该领域的未来研究方向。本综述旨在帮助推荐系统研究人员及实践者更深入理解可视化可解释推荐研究的潜力，并为其在现有及未来推荐系统中系统化设计可视化解释提供支持。