With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks, book rating recommendation, user rating recommendation, and book summary recommendation, and explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios.

翻译：随着以生成式预训练变换器（GPT）为代表的大语言模型技术的持续发展变革，各领域诸多经典场景重新焕发新机遇。本文以ChatGPT为建模对象，首次将大语言模型技术融入典型图书资源理解与推荐场景并付诸实践。通过构建基于ChatGPT的类ChatGPT图书推荐系统框架（BookGPT），本文尝试将ChatGPT应用于图书评分推荐、用户评分推荐及图书摘要推荐三项典型任务的推荐建模，探索大语言模型技术在图书推荐场景中的可行性。同时，基于图书推荐任务的不同评估方案与现有经典推荐模型，本文讨论了BookGPT在图书推荐场景中的优劣，并分析了大语言模型在此类场景中的后续发展机遇与改进方向。