Personalized news recommendation systems often struggle to effectively capture the complexity of user preferences, as they rely heavily on shallow representations, such as article titles and abstracts. To address this problem, we introduce a novel method, namely PNR-LLM, for Large Language Models for Personalized News Recommendation. Specifically, PNR-LLM harnesses the generation capabilities of LLMs to enrich news titles and abstracts, and consequently improves recommendation quality. PNR-LLM contains a novel module, News Enrichment via LLMs, which generates deeper semantic information and relevant entities from articles, transforming shallow contents into richer representations. We further propose an attention mechanism to aggregate enriched semantic- and entity-level data, forming unified user and news embeddings that reveal a more accurate user-news match. Extensive experiments on MIND datasets show that PNR-LLM outperforms state-of-the-art baselines. Moreover, the proposed data enrichment module is model-agnostic, and we empirically show that applying our proposed module to multiple existing models can further improve their performance, verifying the advantage of our design.

本研究解决了个性化新闻推荐系统难以有效捕捉用户偏好复杂性的问题。提出的PNR-LLM方法利用大语言模型的生成能力丰富新闻标题和摘要，从而提升推荐质量。实验表明，PNR-LLM在MIND数据集上优于现有的先进基线，并且其数据丰富模块适用于多种模型，进一步验证了其设计的优势。

通过分层大语言模型提示增强新闻推荐