Toward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective
2026-07-06 • Human-Computer Interaction
Human-Computer InteractionRobotics
AI summaryⓘ
The authors explain that social robots help children in clinical settings and need to personalize their actions to each child. They see this as a recommendation problem but face challenges because it’s hard to get good data about how each child responds to the robot. The authors point out problems like children changing over time, few signals showing if the robot helped, and difficulties linking visits. To fix this, they suggest four guidelines for collecting better data to improve robot personalization.
social robotspersonalizationrecommendation systemsuser profilingdata collectionclinical settingsobservational datauser stateeffectiveness signalsanonymization
Authors
Jin Huang, Eric Nichols, Fethiye Irmak Dogan, Hatice Gunes
Abstract
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.