Explainable recommender systems (RS) have traditionally followed a one-size-fits-all approach, delivering the same explanation level of detail to each user, without considering their individual needs and goals. Further, explanations in RS have so far been presented mostly in a static and non-interactive manner. To fill these research gaps, we aim in this paper to adopt a user-centered, interactive explanation model that provides explanations with different levels of detail and empowers users to interact with, control, and personalize the explanations based on their needs and preferences. We followed a user-centered approach to design interactive explanations with three levels of detail (basic, intermediate, and advanced) and implemented them in the transparent Recommendation and Interest Modeling Application (RIMA). We conducted a qualitative user study (N=14) to investigate the impact of providing interactive explanations with varying level of details on the users' perception of the explainable RS. Our study showed qualitative evidence that fostering interaction and giving users control in deciding which explanation they would like to see can meet the demands of users with different needs, preferences, and goals, and consequently can have positive effects on different crucial aspects in explainable recommendation, including transparency, trust, satisfaction, and user experience.

翻译：可解释推荐系统传统上采用“一刀切”的方式，向每位用户提供相同详细程度的解释，而未考虑其个体需求与目标。此外，现有推荐系统中的解释大多以静态且非交互的方式呈现。为填补这些研究空白，本文旨在采用以用户为中心的交互式解释模型，该模型能提供不同详细程度的解释，并赋予用户根据自身需求与偏好交互、控制及个性化定制解释的能力。我们遵循以用户为中心的方法，设计了三层详细程度（基础、中级和高级）的交互式解释，并将其实现于透明的推荐与兴趣建模应用（RIMA）中。我们开展了一项定性用户研究（N=14），以探究提供不同详细程度的交互式解释对用户感知可解释推荐系统的影响。研究定性证据表明，促进用户交互并赋予其决定所查看解释的控制权，能够满足不同需求、偏好与目标用户的需求，从而对可解释推荐中的关键方面（包括透明度、信任度、满意度及用户体验）产生积极影响。