  Recommendation systems have witnessed significant advancements and have been
widely used over the past decades. However, most traditional recommendation
methods are task-specific and therefore lack efficient generalization ability.
Recently, the emergence of ChatGPT has significantly advanced NLP tasks by
enhancing the capabilities of conversational models. Nonetheless, the
application of ChatGPT in the recommendation domain has not been thoroughly
investigated. In this paper, we employ ChatGPT as a general-purpose
recommendation model to explore its potential for transferring extensive
linguistic and world knowledge acquired from large-scale corpora to
recommendation scenarios. Specifically, we design a set of prompts and evaluate
ChatGPT's performance on five recommendation scenarios. Unlike traditional
recommendation methods, we do not fine-tune ChatGPT during the entire
evaluation process, relying only on the prompts themselves to convert
recommendation tasks into natural language tasks. Further, we explore the use
of few-shot prompting to inject interaction information that contains user
potential interest to help ChatGPT better understand user needs and interests.
Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT
has achieved promising results in certain tasks and is capable of reaching the
baseline level in others. We conduct human evaluations on two
explainability-oriented tasks to more accurately evaluate the quality of
contents generated by different models. And the human evaluations show ChatGPT
can truly understand the provided information and generate clearer and more
reasonable results. We hope that our study can inspire researchers to further
explore the potential of language models like ChatGPT to improve recommendation
performance and contribute to the advancement of the recommendation systems
field.
