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.

翻译：社交机器人中的个性化是指机器人满足个体用户需求和/或偏好的能力。现有方法通常依赖大型语言模型（LLMs）基于用户元数据和历史交互生成上下文感知的响应，或采用强化学习（RL）等自适应方法从用户的实时即时反应中学习。然而，这些方法未能全面捕捉用户偏好——包括长期、短期和细粒度方面——并利用这些偏好来排序和选择动作、主动个性化交互以及确保符合伦理责任的适应性调整。为应对这些局限，我们建议借鉴专门用于建模用户偏好并提供个性化推荐的推荐系统（RSs）。为确保RS技术在整个社交机器人流程中得到充分且无缝的整合，我们（i）对齐社交机器人与RSs的基本范式，（ii）识别能够增强社交机器人个性化的关键技术，以及（iii）将其设计为模块化、即插即用的组件。这项工作不仅为将RS技术整合到社交机器人中建立了一个框架，也为RS与人机交互（HRI）社区之间的深度合作开辟了道路，从而加速两个领域的创新。