BookGPT: A General Framework for Book Recommendation Empowered by Large
  Language Model
Aakas Zhiyuli Yanfang Chen Xuan Zhang Xun Liang
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.