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News Recommendation with Category Description by a Large Language Model
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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.
Forward citations
Cited by 3 Pith papers
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A Survey on LLM-based News Recommender Systems
A survey that categorizes LLM-based news recommender systems and reports benchmark comparisons on MIND and Adressa.
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Revisiting Language Models in Neural News Recommender Systems
Larger language models as news encoders do not consistently improve recommendation accuracy, but they do improve performance for cold-start users, at higher fine-tuning and compute cost.
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Large Language Model Enhanced Recommender Systems: A Survey
A survey organizing LLM-enhanced recommender systems into knowledge, interaction, and model enhancement, and tracing a shift from explicit text to implicit embeddings and fine-tuned open-source LLMs.
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