重新构想社交机器人作为推荐系统：基础、框架与应用
Reimagining Social Robots as Recommender Systems: Foundations, Framework, and Applications
AI总结：
本文提出将推荐系统应用于社交机器人，以提升个性化能力，通过整合RS技术建立框架并促进跨领域合作。
摘要：
社交机器人中的个性化指的是机器人能够满足个体用户的需求和/或偏好。现有方法通常依赖于大型语言模型（LLMs）根据用户元数据和历史交互生成上下文感知响应，或使用强化学习（RL）等自适应方法实时学习用户的即时反应。然而，这些方法在全面捕捉用户偏好（包括长期、短期和细粒度方面）以及利用这些偏好进行动作排序和选择、主动个性化交互和确保伦理负责任的适应方面存在不足。为了解决这些局限性，我们提出借鉴推荐系统（RSs），它们专长于建模用户偏好并提供个性化推荐。为了确保RS技术的整合是坚实且无缝的，我们（i）对社交机器人和RSs的底层范式进行对齐，（ii）识别可以增强社交机器人个性化的方法，（iii）将它们设计为模块化、即插即用的组件。本工作不仅建立了将RS技术整合到社交机器人中的框架，还为RS和人机交互（HRI）社区之间的深入合作开辟了道路，加速了两个领域的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
