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.

本文介绍一种基于大型语言模型的可生成新闻推荐框架 GENRE，通过预训练的语义知识，提供了一种灵活且统一的新闻推荐解决方案，可以用于个性化新闻推荐、用户画像和新闻摘要。各种流行的推荐模型的广泛实验证明了 GENRE 的有效性。