Explanations are central to improving transparency, trust, and user satisfaction in recommender systems (RS), yet it remains unclear how different explanation formats (visual vs. textual) are suited to users with different personal characteristics (PCs). To this end, we report a within-subject user study (n=54) comparing visual and textual explanations and examine how explanation format and PCs jointly influence perceived control, transparency, trust, and satisfaction in an educational recommender system (ERS). Using robust mixed-effects models, we analyze the moderating effects of a wide range of PCs, including Big Five traits, need for cognition, decision making style, visualization familiarity, and technical expertise. Our results show that a well-designed visual, simple, interactive, selective, easy to understand visualization that clearly and intuitively communicates how user preferences are linked to recommendations, fosters perceived control, transparency, appropriate trust, and satisfaction in the ERS for most users, independent of their PCs. Moreover, we derive a set of guidelines to support the effective design of explanations in ERSs.

翻译：在推荐系统中，解释对于提升系统透明度、用户信任度和满意度至关重要，但目前尚不清楚不同解释格式（可视化 vs. 文本）如何适配具有不同个人特征的用户。为此，我们开展了一项受试者内用户研究（n=54），比较可视化解释与文本解释，并考察解释格式与个人特征如何共同影响用户在教育推荐系统中的感知控制感、透明度、信任度和满意度。通过稳健混合效应模型，我们分析了广泛个人特征（包括大五人格特质、认知需求、决策风格、可视化熟悉程度和技术专长）的调节效应。结果表明：精心设计的、直观且清晰地展示用户偏好与推荐结果之间关联的可视化解释（具有视觉简洁、交互性强、可选择性好、易于理解的特点），能够提升大多数用户在教育推荐系统中的感知控制感、透明度、适当信任度和满意度，且不受其个人特征影响。此外，我们总结了一套指导原则，以支持教育推荐系统中解释的有效设计。