Toward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective

2026-07-06

AI Summary

This work addresses the challenges of data scarcity and dynamic user state modeling in personalized social robots for child health interventions, where weak within-conversation feedback, difficulty linking identities across sessions, and observational bias hinder effective personalization. Drawing from recommender systems research, the paper introduces four novel data collection principles centered on child well-being: integrated user profiling, validity signals, linkable coverage, and real-time exposure logging. It proposes a recommendation framework grounded in user profiles and ranking models, explicitly aligning each robotic capability with specific data requirements. By translating these requirements into actionable data collection guidelines under the paradigm of responsible computing, the study lays a foundational infrastructure for empirical research and real-world deployment of socially assistive robots for children.

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.

Problem

• personalization
• social robots
• child well-being
• recommender systems
• data requirements

Innovation

• personalized social robots
• recommender systems
• data collection principles
• child well-being
• observational data