Abstract
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.
Abstract (translated)
随着大型语言模型(LLM)技术的不断发展和变化，以生成预训练Transformers(GPTs)为代表的许多领域经典场景再次出现了新的机会。本文将ChatGPT作为建模对象，首次将LLM技术融入典型的书籍资源理解和推荐场景，并将其实际应用中。通过基于ChatGPT构建一个类似于ChatGPT的书籍推荐系统(BookGPT)框架，本文尝试将ChatGPT应用于三个典型的任务：书籍评价推荐、用户评价推荐和书籍摘要推荐，并探索LLM技术在书籍推荐场景中的可行性。同时，基于书籍推荐任务的不同评估方法和现有经典推荐模型，本文讨论了BookGPT在书籍推荐场景中的优点和缺点，并分析在这些场景中后续LLM的机会和改进方向。
URL
https://arxiv.org/abs/2305.15673
