Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with the RS. Justification and transparency represent two crucial goals in explainable recommendation. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referred to as intelligibility types) to understand results given by the RS. In this paper, we identify relationships between Why and How explanation intelligibility types and the explanation goals of justification and transparency. We followed the Human-Centered Design (HCD) approach and leveraged the What-Why-How visualization framework to systematically design and implement Why and How visual explanations in the transparent Recommendation and Interest Modeling Application (RIMA). Furthermore, we conducted a qualitative user study (N=12) to investigate the potential effects of providing Why and How explanations together in an explainable RS on the users' perceptions regarding transparency, trust, and satisfaction. Our study showed qualitative evidence confirming that the choice of the explanation intelligibility types depends on the explanation goal and user type.

翻译：增强推荐系统（RS）的解释功能以帮助用户做出明智决策、提升对RS的信任与满意度，已受到广泛关注。理由性和透明性是可解释推荐中的两个核心目标。与忠实地揭示推荐机制背后推理过程的透明性不同，理由性传达的是一种概念模型，其可能不同于底层算法所依据的模型。解释是对问题的回答。在可解释推荐中，用户希望提出问题（即可理解类型）以理解RS给出的结果。本文探讨了“为何”（Why）与“如何”（How）两种解释可理解类型与理由性及透明性这两个解释目标之间的关系。我们遵循以人为中心的设计（HCD）方法，并利用“是什么-为什么-怎么办”（What-Why-How）可视化框架，系统地设计并实现了透明推荐与兴趣建模应用（RIMA）中的“为何”与“如何”视觉解释。此外，我们开展了一项定性用户研究（N=12），探究在可解释RS中同时提供“为何”与“如何”两种解释对用户关于透明度、信任度和满意度的感知可能产生的影响。研究提供了定性证据，证实了解释可理解类型的选择取决于解释目标与用户类型。