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

LKPNR: LLM and KG for Personalized News Recommendation Framework