BookGPT：大语言模型赋能的图书推荐通用框架,Electronics

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BookGPT：大语言模型赋能的图书推荐通用框架
Electronics ( IF 2.6 ) Pub Date : 2023-11-15 , DOI: 10.3390/electronics12224654
Zhiyu Li ₁ , Yanfang Chen ₂ , Xuan Zhang ₃ , Xun Liang ₃

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随着以生成式预训练变压器（GPT）为代表的大语言模型（LLM）技术的不断发展和变化，各个领域的许多经典场景重新出现并带来了新的机遇。本文以ChatGPT为建模对象，首次将LLM技术融入到典型的图书资源理解和推荐场景中，并付诸实践。本文通过在ChatGPT的基础上构建类ChatGPT的图书推荐系统（BookGPT）框架，尝试将ChatGPT应用于图书评分推荐、用户评分推荐和图书摘要推荐三个典型任务的推荐建模；并探讨了LLM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和现有的经典推荐模型，讨论了BookGPT在图书推荐场景中的优缺点，并分析了后续LLM在这些场景中的机会和改进方向。实验研究表明：（1）BookGPT在现有的经典图书推荐任务中能够取得良好的推荐结果。尤其是在待推荐的目标对象信息较少的情况下，例如零样本或单样本学习任务，BookGPT的性能接近甚至优于当前经典的图书推荐算法，并且该方法具有很大的改进潜力。（2）在书籍摘要推荐等文本生成任务中，BookGPT模型的推荐效果优于豆瓣阅读的手动编辑过程，甚至可以根据读者属性和身份信息进行个性化的可解读内容推荐，使其比可解释的一刀切的推荐模型更有说服力。最后，我们开源了相关数据集和实验代码，希望本文提出的探索方案能够启发更多LLM的发展，拓展其在图书推荐和一般推荐任务领域的应用和理论研究前景。

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BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model

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 the book summary recommendation; it also 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. The experimental research shows the following: (1) The BookGPT can achieve good recommendation results in existing classic book recommendation tasks. Especially in cases containing less information about the target object to be recommended, such as zero-shot or one-shot learning tasks, the performance of the BookGPT is close to or even better than that of the current classic book recommendation algorithms, and this method has great potential for improvement. (2) In text generation tasks such as book summary recommendation, the recommendation effect of the BookGPT model is better than that of the manual editing process of Douban Reading, and it can even perform personalized interpretable content recommendations based on readers’ attribute and identity information, making it more persuasive than interpretable one-size-fits-all recommendation models. Finally, we have open-sourced the relevant datasets and experimental codes, hoping that the exploratory program proposed in this paper can inspire the development of more LLMs to expand their applications and theoretical research prospects in the field of book recommendation and general recommendation tasks.

更新日期：2023-11-15

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