  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.
