视觉解释比文字更易提升教育推荐系统信任感与满意度。
Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System
- 对比视觉与文字解释，测试用户对推荐逻辑的理解度。
- 多数用户在视觉解释下感知控制力和信任度更高。
- 适合教育推荐系统设计者参考，尤其关注用户差异。
解释是提升推荐系统透明度、信任度和用户满意度的核心，但不同解释形式（视觉或文字）对具有不同个人特征（PCs）的用户是否适用仍不明确。为此，我们开展一项配对用户研究（n=54），比较视觉与文字解释在教育推荐系统（ERS）中的效果，并分析解释形式与个人特征共同影响感知控制、透明度、信任与满意度的作用机制。采用稳健的混合效应模型，考察了包括大五人格、认知需求、决策风格、可视化熟悉度和技术专长在内的多种个人特征的调节作用。结果表明，设计良好、简洁、交互性强、选择性突出且易于理解的视觉解释，能清晰直观地展现用户偏好与推荐之间的关联，显著增强大多数用户的感知控制、透明度、适当信任及满意度，且不受个人特征影响。此外，研究提炼出一套指导教育推荐系统解释设计的有效准则。
原文摘要 · Abstract (English)
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.
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