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细节层次的解释，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、非交互的方式呈现。为填补上述研究空白，本文拟采用以用户为中心的交互式解释模型，该模型能够提供不同细节层次的解释，并赋予用户根据自身需求和偏好对解释进行交互、控制及个性化的能力。我们遵循以用户为中心的方法，设计了三种细节层次（基础层、中间层、高级层）的交互式解释，并在透明推荐与兴趣建模应用系统中加以实现。通过一项包含14名参与者的定性用户研究，我们探究了提供可变细节层次的交互式解释对用户感知可解释推荐系统的影响。研究结果显示，定性证据表明，增强交互性并赋予用户决定查看何种解释的控制权，能够满足具有不同需求、偏好与目标的用户需求，进而对可解释推荐中的透明度、信任度、满意度及用户体验等关键维度产生积极影响。