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：为推荐提供系统生成的解释，是实现透明且可信赖推荐系统的重要一步。可解释推荐系统为其输出提供人类可理解的依据。过去二十年中，可解释推荐在推荐系统研究领域备受关注。本文旨在全面综述推荐系统中视觉解释的研究工作。具体而言，我们基于四个维度（即解释目标、解释范围、解释风格和解释格式）系统回顾了推荐系统中关于解释的文献。鉴于可视化的重要性，我们从解释性可视化的角度审视推荐系统文献，即将可视化作为解释的展示风格。由此，我们推导出一系列可能对推荐系统中解释性可视化设计具有建设性的指南，并指出了该领域未来的研究方向。本综述旨在帮助推荐研究人员和实践者更好地理解视觉可解释推荐研究的潜力，并支持他们在当前及未来的推荐系统中系统地设计视觉解释。