为社交媒体推荐设计用户对齐的可视化解释系统
Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations
- 根据用户身份和场景动态调整解释形式与颗粒度
- 支持专家用技术细节版，普通用户用简化视觉版
- 首个在单一流程中融合视觉与数值解释的框架
当前社交媒体平台依赖AI推荐提升用户体验，但用户因不理解推荐原因而失去信任。现有解释普遍缺乏对用户需求和使用场景的适配。本文提出一种用户分层、上下文感知的可视化解释框架，通过多样化解释方法，在同一管道中动态调整解释风格（视觉/数值）与粒度（专家/普通用户）。系统可针对不同用户呈现技术详尽或简洁直观的解释形式。一项包含30名真实用户的公开试点将验证其对决策质量与信任感的影响。
原文摘要 · Abstract (English)
Social media platforms today strive to improve user experience through AI recommendations, yet the value of such recommendations vanishes as users do not understand the reasons behind them. This issue arises because explainability in social media is general and lacks alignment with user-specific needs. In this vision paper, we outline a user-segmented and context-aware explanation layer by proposing a visual explanation system with diverse explanation methods. The proposed system is framed by the variety of user needs and contexts, showing explanations in different visualized forms, including a technically detailed version for AI experts and a simplified one for lay users. Our framework is the first to jointly adapt explanation style (visual vs. numeric) and granularity (expert vs. lay) inside a single pipeline. A public pilot with 30 X users will validate its impact on decision-making and trust.
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