Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge. However, existing methods still lack domain knowledge and struggle to capture users' fine-grained preferences. Meanwhile, many traditional SR methods improve this issue by integrating side information while suffering from information loss. To summarize, we believe that a good recommendation system should utilize both general and domain knowledge simultaneously. Therefore, we introduce an external knowledge base and propose Knowledge Prompt-tuning for Sequential Recommendation (\textbf{KP4SR}). Specifically, we construct a set of relationship templates and transform a structured knowledge graph (KG) into knowledge prompts to solve the problem of the semantic gap. However, knowledge prompts disrupt the original data structure and introduce a significant amount of noise. We further construct a knowledge tree and propose a knowledge tree mask, which restores the data structure in a mask matrix form, thus mitigating the noise problem. We evaluate KP4SR on three real-world datasets, and experimental results show that our approach outperforms state-of-the-art methods on multiple evaluation metrics. Specifically, compared with PLM-based methods, our method improves NDCG@5 and HR@5 by \textcolor{red}{40.65\%} and \textcolor{red}{36.42\%} on the books dataset, \textcolor{red}{11.17\%} and \textcolor{red}{11.47\%} on the music dataset, and \textcolor{red}{22.17\%} and \textcolor{red}{19.14\%} on the movies dataset, respectively. Our code is publicly available at the link: \href{https://github.com/zhaijianyang/KP4SR}{\textcolor{blue}{https://github.com/zhaijianyang/KP4SR}.}

翻译：预训练语言模型（PLMs）在序列推荐（SR）中展现了强大的性能，被用于提取通用知识。然而，现有方法仍缺乏领域知识，难以捕捉用户的细粒度偏好。同时，许多传统序列推荐方法通过整合辅助信息来改善这一问题，却面临信息丢失的困境。总之，我们认为一个好的推荐系统应同时利用通用知识和领域知识。因此，我们引入外部知识库，并提出知识提示调优用于序列推荐（\textbf{KP4SR}）。具体而言，我们构建一组关系模板，将结构化知识图谱（KG）转换为知识提示，以解决语义鸿沟问题。然而，知识提示会破坏原始数据结构并引入大量噪声。我们进一步构建知识树并提出知识树掩码，以掩码矩阵形式恢复数据结构，从而缓解噪声问题。我们在三个真实数据集上评估KP4SR，实验结果表明，我们的方法在多个评估指标上优于最先进的方法。具体而言，与基于PLM的方法相比，我们的方法在图书数据集上分别将NDCG@5和HR@5提升了\textcolor{red}{40.65\%}和\textcolor{red}{36.42\%}，在音乐数据集上提升了\textcolor{red}{11.17\%}和\textcolor{red}{11.47\%}，在电影数据集上提升了\textcolor{red}{22.17\%}和\textcolor{red}{19.14\%}。我们的代码已公开在链接：\href{https://github.com/zhaijianyang/KP4SR}{\textcolor{blue}{https://github.com/zhaijianyang/KP4SR}.}