为儿童心理健康机器人设计数据收集原则，解决个性化推荐的数据难题。
Toward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective
- 从推荐系统视角提出个性化机器人交互的数据构建框架。
- 指出儿童状态变化快、信号弱、跨会话难关联等核心数据挑战。
- 提出四条可操作数据采集原则，适合医疗机器人研发与伦理研究者。
社交机器人在临床环境中日益用于支持儿童福祉，其有效干预必须个性化。个性化可视为推荐问题，近期提出的推荐系统框架通过用户画像、排序与负责任计算提供理论指导。但实际应用受限于数据：儿童状态在会话内与跨会话间持续变化，无法用固定描述表征；会话内机器人行为效果的信号微弱且间接；跨会话中儿童极少重复出现，匿名化又破坏身份连续性；因无法随机干预，现有数据均为观察性，偏向已执行行为。这些均属典型推荐系统难题。本文提出四项数据原则应对：集成化用户画像、有效性信号、可链接覆盖范围、采集时记录曝光日志。明确各能力所需原则，并转化为具体数据采集指南。
原文摘要 · Abstract (English)
Social robots are increasingly deployed in clinical settings to support the well-being of children, where effective support must be personalized to each child. Personalization, choosing the robot action best suited to each child, can be framed as a recommendation problem, and a recently proposed recommender-system framework for social robots offers a principled approach through user profiling, ranking, and responsible computing. Instantiating it, however, is blocked not by the model but by the data, which is hard to gather. A child's state shifts within and across visits, so no fixed description of the user holds. Within a session, the few signals of whether the robot's actions helped are weak and indirect. Across sessions, children are rarely seen more than once, and anonymization breaks the identity needed to link visits. Because care cannot be randomized, existing data is observational, biased toward whatever was already done. Each is a familiar recommender-system problem, and we propose four data principles in response: an integrated profile, effectiveness signals, linkable coverage, and an exposure record logged at collection time. We identify which of these principles each capability requires, and frame them as concrete guidelines for data collection.
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