Toward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective
For researchers and practitioners developing personalized social robots for child well-being, this work provides actionable data collection principles to overcome practical bottlenecks, though it remains at the conceptual stage without empirical validation.
The paper identifies data collection challenges for personalizing social robots to support child well-being, such as shifting user states, weak feedback signals, and observational bias. It proposes four data principles—integrated profile, effectiveness signals, linkable coverage, and exposure record—to address these issues, framing them as concrete guidelines for data collection.
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.