Recommender systems aim to help users find relevant items more quickly by providing personalized recommendations. Explanations in recommender systems help users understand why such recommendations have been generated, which in turn makes the system more transparent and promotes users' trust and satisfaction. In recent years, explaining recommendations has drawn increasing attention from both academia and from industry. In this paper, we present a user study to investigate context-aware explanations in recommender systems. In particular, we build a web-based questionnaire that is able to interact with users: generating and explaining recommendations. With this questionnaire, we investigate the effects of context-aware explanations in terms of efficiency, effectiveness, persuasiveness, satisfaction, trust and transparency through a user study. Besides, we propose a novel method based on fuzzy synthetic evaluation for aggregating these metrics.

翻译：推荐系统旨在通过提供个性化推荐，帮助用户更快地找到相关项目。推荐系统中的解释有助于用户理解推荐生成的原因，从而增强系统透明度，提升用户信任度与满意度。近年来，推荐解释研究日益受到学术界与工业界的关注。本文通过用户研究探讨推荐系统中的情境感知解释。具体而言，我们构建了一个能够与用户交互的基于网络的问卷系统：该系统可生成推荐并解释推荐理由。通过该问卷，我们以用户研究的方式，从效率、有效性、说服力、满意度、信任度及透明度等多个维度评估情境感知解释的效果。此外，我们提出了一种基于模糊综合评估的新方法，用于整合上述多维度评估指标。