Recent popularity surrounds large AI language models due to their impressive
natural language capabilities. They contribute significantly to
language-related tasks, including prompt-based learning, making them valuable
for various specific tasks. This approach unlocks their full potential,
enhancing precision and generalization. Research communities are actively
exploring their applications, with ChatGPT receiving recognition.
Despite extensive research on large language models, their potential in
recommendation scenarios still needs to be explored. This study aims to fill
this gap by investigating ChatGPT's capabilities as a zero-shot recommender
system. Our goals include evaluating its ability to use user preferences for
recommendations, reordering existing recommendation lists, leveraging
information from similar users, and handling cold-start situations. We assess
ChatGPT's performance through comprehensive experiments using three datasets
(MovieLens Small, Last.FM, and Facebook Book).
We compare ChatGPT's performance against standard recommendation algorithms
and other large language models, such as GPT-3.5 and PaLM-2. To measure
recommendation effectiveness, we employ widely-used evaluation metrics like
Mean Average Precision (MAP), Recall, Precision, F1, normalized Discounted
Cumulative Gain (nDCG), Item Coverage, Expected Popularity Complement (EPC),
Average Coverage of Long Tail (ACLT), Average Recommendation Popularity (ARP),
and Popularity-based Ranking-based Equal Opportunity (PopREO).
Through thoroughly exploring ChatGPT's abilities in recommender systems, our
study aims to contribute to the growing body of research on the versatility and
potential applications of large language models. Our experiment code is
available on the GitHub repository:
this https URL

本研究旨在填补大型语言模型在推荐场景中的潜力研究空白，并通过实验评估 ChatGPT 在零样本推荐系统中的能力及其与标准推荐算法以及其他大型语言模型的对比表现。