Reimagining Social Robots as Recommender Systems: Foundations, Framework, and Applications

2026-01-27

AI Summary

Current social robots struggle to effectively model users’ long-term, short-term, and fine-grained preferences, limiting their ability to engage in proactive, personalized, and ethically controllable interactions. This work addresses this gap by systematically introducing the recommender systems paradigm into social robotics for the first time, proposing a modular, plug-and-play integration framework. By synergistically combining user preference modeling, ranking algorithms, large language models, and adaptive learning mechanisms, the framework enables precise, interpretable, and responsible personalization. The resulting architecture not only enhances the proactiveness and adaptability of human–robot interaction but also establishes a scalable and reusable paradigm that fosters collaborative innovation between recommender systems and social robotics research.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Personalization in social robots refers to the ability of the robot to meet the needs and/or preferences of an individual user. Existing approaches typically rely on large language models (LLMs) to generate context-aware responses based on user metadata and historical interactions or on adaptive methods such as reinforcement learning (RL) to learn from users'immediate reactions in real time. However, these approaches fall short of comprehensively capturing user preferences-including long-term, short-term, and fine-grained aspects-, and of using them to rank and select actions, proactively personalize interactions, and ensure ethically responsible adaptations. To address the limitations, we propose drawing on recommender systems (RSs), which specialize in modeling user preferences and providing personalized recommendations. To ensure the integration of RS techniques is well-grounded and seamless throughout the social robot pipeline, we (i) align the paradigms underlying social robots and RSs, (ii) identify key techniques that can enhance personalization in social robots, and (iii) design them as modular, plug-and-play components. This work not only establishes a framework for integrating RS techniques into social robots but also opens a pathway for deep collaboration between the RS and HRI communities, accelerating innovation in both fields.

Problem

• social robots
• personalization
• recommender systems
• user preferences
• human-robot interaction

Innovation

• Recommender Systems
• Social Robots
• Personalization
• Modular Framework
• Human-Robot Interaction