News Recommendation with Category Description by a Large Language Model

TL;DR

This paper introduces a method that uses a large language model to automatically generate detailed category descriptions for news articles, enhancing recommendation accuracy on online news platforms.

Contribution

It presents a novel approach to automatically generate and incorporate category descriptions using an LLM, improving news recommendation performance without manual effort.

Findings

Achieved up to 5.8% improvement in AUC over baseline models

Validated effectiveness across multiple state-of-the-art recommendation models

Demonstrated benefits of LLM-generated descriptions in news recommendation

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.