Sparks of Artificial General Recommender (AGR): Early Experiments with
  ChatGPT
Guo Lin Yongfeng Zhang
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.