News Recommendation with Category Description by a Large Language Model

• URL: http://arxiv.org/abs/2405.13007v1
• Date: Mon, 13 May 2024 08:53:43 GMT
• Title: News Recommendation with Category Description by a Large Language Model
• Authors: Yuki Yada, Hayato Yamana,
• Abstract summary: News categories, such as tv-golden-globe, finance-real-estate, and news-politics, play an important role in understanding news content. We propose a novel method that automatically generates informative category descriptions using a large language model. Our method successfully achieved 5.8% improvement at most in AUC compared with baseline approaches.
• Score: 1.6267479602370543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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