News Recommendation with Category Description by a Large Language Model
<|reference_start|>News Recommendation with Category Description by a Large Language Model: Personalized news recommendations are essential for online news platforms to assist users in discovering news articles that match their interests from a vast amount of online content. Appropriately encoded content features, such as text, categories, and images, are essential for recommendations. Among these features, news categories, such as tv-golden-globe, finance-real-estate, and news-politics, play an important role in understanding news content, inspiring us to enhance the categories' descriptions. In this paper, we propose a novel method that automatically generates informative category descriptions using a large language model (LLM) without manual effort or domain-specific knowledge and incorporates them into recommendation models as additional information. In our comprehensive experimental evaluations using the MIND dataset, our method successfully achieved 5.8% improvement at most in AUC compared with baseline approaches without the LLM's generated category descriptions for the state-of-the-art content-based recommendation models including NAML, NRMS, and NPA. These results validate the effectiveness of our approach. The code is available at https://github.com/yamanalab/gpt-augmented-news-recommendation.<|reference_end|>
@article{yada2024news,
    title={News Recommendation with Category Description by a Large Language Model},
    author={Yuki Yada and Hayato Yamana},
    journal={arXiv preprint arXiv:2405.13007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13007},
    primaryClass={cs.CL cs.AI cs.IR cs.LG}
}