Open Access

ARTICLE

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Hao Chen^#, Runfeng Xie^#, Xiangyang Cui, Zhou Yan, Xin Wang, Zhanwei Xuan^*, Kai Zhang^*

State Key Laboratory of Communication Content Cognition, People’s Daily Online, Beijing, 100733, China

* Corresponding Authors: Zhanwei Xuan. Email: ; Kai Zhang. Email:

(This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)

Computers, Materials & Continua 2024, 79(3), 4283-4296. https://doi.org/10.32604/cmc.2024.049129

Received 28 December 2023; Accepted 15 April 2024; Issue published 20 June 2024

Abstract

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems. Traditional methods are usually difficult to learn and acquire 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 traditional methods. To learn the contextual information of news text, we use LLMs’ powerful text understanding ability to generate news representations with rich semantic information, and then, the generated news representations are used to enhance the news encoding in traditional methods. In addition, multi-hops relationship of news entities is mined and the structural information of news is encoded using KG, thus alleviating the challenge of long-tail distribution. Experimental results demonstrate that compared with various traditional models, on evaluation indicators such as AUC, MRR, nDCG@5 and nDCG@10, the framework significantly improves the recommendation performance. The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation. Our code is available at .

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Keywords

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