Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user's context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user's personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user's context.

翻译：尽管学界承认不同终端用户对解释的感知可能存在显著差异，但可解释推荐系统（RS）长期以来遵循"一刀切"模型——即向所有用户提供相同粒度的解释，未考虑个体用户的特定情境（如目标与个人特征）。为填补这一研究空白，本文旨在实现从"一刀切"到个性化可解释推荐的范式转变，给予用户自主选择解释类型的权利。我们开发了透明的推荐与兴趣建模应用（RIMA），该应用可按需提供个性化推荐解释，通过基础级、进阶级、高级三种信息粒度满足不同类型终端用户的需求。通过开展被试内实验（N=31），我们探究了用户个人特征与解释粒度之间的关系，以及这两个变量对用户感知可解释推荐系统的影响（针对不同解释目标）。研究结果表明，不同粒度的可解释推荐系统感知效果受解释目标和用户类型不同程度的调节。据此，我们提出了一系列理论建议与设计指南，以支撑面向用户情境的系统化可解释RS界面设计。