正面推荐解释比正反结合更受用户欢迎，尤其在看片场景中。
Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations
- 用纯正向特征解释电影推荐，不掺负面信息
- 129人实验显示正向解释在信任等维度得分更高
- 适合娱乐类推荐，需注意熟悉度等干扰因素
推荐系统在流媒体服务中帮助用户发现内容，但其效果依赖用户理解推荐原因。本研究仅基于物品特征生成解释，未使用个性化数据，模拟真实推荐场景。通过在线实验（共129名参与者），对比了单一正向与正反结合的特征解释对用户感知的影响。结果表明，正向解释在可信度、透明度、有效性及满意度方面均获得更高评价。研究提示，在低风险娱乐场景如热门电影推荐中，简洁的正向解释可能更有效。但需谨慎解读，因可能存在项目熟悉度、负向信息位置等混杂因素影响。该工作为推荐界面的解释设计提供实践指导，并强调情境对用户偏好的重要性。
原文摘要 · Abstract (English)
Recommender systems play a vital role in helping users discover content in streaming services, but their effectiveness depends on users understanding why items are recommended. In this study, explanations were based solely on item features rather than personalized data, simulating recommendation scenarios. We compared user perceptions of one-sided (purely positive) and two-sided (positive and negative) feature-based explanations for popular movie recommendations. Through an online study with 129 participants, we examined how explanation style affected perceived trust, transparency, effectiveness, and satisfaction. One-sided explanations consistently received higher ratings across all dimensions. Our findings suggest that in low-stakes entertainment domains such as popular movie recommendations, simpler positive explanations may be more effective. However, the results should be interpreted with caution due to potential confounding factors such as item familiarity and the placement of negative information in explanations. This work provides practical insights for explanation design in recommender interfaces and highlights the importance of context in shaping user preferences.
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