  Accurately recommending candidate news articles to users is a basic challenge
faced by personalized news recommendation systems. Traditional methods are
usually difficult to grasp the complex semantic information in news texts,
resulting in unsatisfactory recommendation results. Besides, these traditional
methods are more friendly to active users with rich historical behaviors.
However, they can not effectively solve the "long tail problem" of inactive
users. To address these issues, this research presents a novel general
framework that combines Large Language Models (LLM) and Knowledge Graphs (KG)
into semantic representations of traditional methods. In order to improve
semantic understanding in complex news texts, we use LLMs' powerful text
understanding ability to generate news representations containing rich semantic
information. In addition, our method combines the information about news
entities and mines high-order structural information through multiple hops in
KG, thus alleviating the challenge of long tail distribution. Experimental
results demonstrate that compared with various traditional models, the
framework significantly improves the recommendation effect. The successful
integration of LLM and KG in our framework has established a feasible path for
achieving more accurate personalized recommendations in the news field. Our
code is available at https://github.com/Xuan-ZW/LKPNR.
