让用户与AI共同编辑兴趣档案，提升推荐透明度与信任感
Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders
- 设计可编辑的AI生成观影兴趣摘要，支持用户反馈修正
- 1775名用户参与8周实验，发现用户感知与系统推断存在持续偏差
- 适合关注推荐可解释性与人机协作的研究者和产品设计者
推荐系统中基于自然语言的用户画像因其可解释性，有助于用户审视和优化自身兴趣，从而提升推荐质量。本文提出一种人机协同的电影推荐用户画像系统，通过展示用户观影历史生成的个性化兴趣摘要，允许用户直接查看、修改并反思系统推断结果。不同于静态画像，该设计促进用户主动参与。在为期八周的在线实地部署中，共吸引1775名活跃用户参与，结果显示用户自我认知与系统推断之间存在持续差异，且该画像显著提升了用户参与度与反思行为。研究进一步识别出利用不完美AI画像激发用户干预的设计方向，为构建更透明、可信的推荐体验提供实践依据。
原文摘要 · Abstract (English)
Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby improving recommendation quality. Building on this foundation, we introduce a human-AI collaborative profile for a movie recommender system that presents editable personalized interest summaries of a user's movie history. Unlike static profiles, this design invites users to directly inspect, modify, and reflect on the system's inferences. In an eight-week online field deployment with 1775 active movie recommender users, we find persistent gaps between user-perceived and system-inferred interests, show how the profile encourages engagement and reflection, and identify design directions for leveraging imperfect AI-powered user profiles to stimulate more user intervention and build more transparent and trustworthy recommender experiences.
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