Personalized news recommendation systems have become essential tools for users to navigate the vast amount of online news content, yet existing news recommenders face significant challenges such as the cold-start problem, user profile modeling, and news content understanding. Previous works have typically followed an inflexible routine to address a particular challenge through model design, but are limited in their ability to understand news content and capture user interests. In this paper, we introduce GENRE, an LLM-powered generative news recommendation framework, which leverages pretrained semantic knowledge from large language models to enrich news data. Our aim is to provide a flexible and unified solution for news recommendation by moving from model design to prompt design. We showcase the use of GENRE for personalized news generation, user profiling, and news summarization. Extensive experiments with various popular recommendation models demonstrate the effectiveness of GENRE. We will publish our code and data for other researchers to reproduce our work.

翻译：个性化新闻推荐系统已成为用户浏览海量在线新闻内容的重要工具，然而现有新闻推荐系统仍面临冷启动、用户画像建模及新闻内容理解等重大挑战。以往研究通常遵循固定范式，通过模型设计解决特定难题，但在新闻内容理解和用户兴趣捕捉方面存在局限。本文提出GENRE——一种基于大语言模型的生成式新闻推荐框架，该框架利用大语言模型中的预训练语义知识来丰富新闻数据。我们的目标是通过从模型设计转向提示设计，为新闻推荐提供灵活统一的解决方案。我们展示了GENRE在个性化新闻生成、用户画像和新闻摘要中的应用，并通过与多种主流推荐模型的大量对比实验验证了其有效性。相关代码与数据将公开发布，以供研究者复现本研究。