A First Look at LLM-powered Generative News Recommendation

Oct. 20^th, 2023: The expanded version, i.e., ONCE, got accepted by WSDM ‘24.
Oct. 10^th, 2023: Got introduced on DataFunTalk!

Qijiong LIU, Nuo CHEN, Tetsuya SAKAI, and Xiao-Ming WU^#
[Code] [Paper]

Abstract

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.

Citation

@misc{liu2023look,
      title={A First Look at LLM-Powered Generative News Recommendation}, 
      author={Qijiong Liu and Nuo Chen and Tetsuya Sakai and Xiao-Ming Wu},
      year={2023},
      eprint={2305.06566},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}