This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domains. We propose ten fundamental principles that an AGR should adhere to, each with its corresponding testing protocols. We proceed to assess whether ChatGPT, a sophisticated LLM, can comply with the proposed principles by engaging in recommendation-oriented dialogues with the model while observing its behavior. Our findings demonstrate the potential for ChatGPT to serve as an AGR, though several limitations and areas for improvement are identified.

翻译：本研究探讨了在大型语言模型（LLMs）最新进展推动下开发通用推荐系统（AGR）的可行性。AGR兼具对话性与通用性，能够进行自然对话并跨领域生成推荐。我们提出了AGR应遵循的十项基本原则，并针对每项原则设计了相应的测试方案。通过引导推荐导向的对话并观察模型行为，我们评估了先进LLM——ChatGPT是否符合所提出的原则。研究结果表明，ChatGPT具备充当AGR的潜力，但同时也揭示了若干局限性与待改进方向。