A Survey on LLM-based News Recommender Systems
Rongyao Wang, Veronica Liesaputra, Zhiyi Huang
TL;DR
This survey addresses the emergence of LLM-based news recommender systems and provides a unified framework to categorize methods into news-oriented, user-oriented, and prediction-oriented modeling. It compares DLLM-based and GLLM-based approaches against traditional deep learning models through extensive experiments on standard datasets, using metrics such as $AUC$, $MRR$, and $nDCG@k$ to assess classification and ranking, plus diversity and personalization. Key findings show that on MIND, several LLM-based models (e.g., MANNeR, LKPNR) can surpass DL baselines in classification and ranking, while on Adressa multilingual challenges can hinder performance; DLLMs often underperform GLLMs on small datasets, and GLLMs require substantial resources. The paper outlines challenges (datasets, benchmarks, methodology) and provides future directions including informative multi-modal multilingual datasets, efficient training, trustworthy and social-driven LLM-based recommendations.
Abstract
News recommender systems play a critical role in mitigating the information overload problem. In recent years, due to the successful applications of large language model technologies, researchers have utilized Discriminative Large Language Models (DLLMs) or Generative Large Language Models (GLLMs) to improve the performance of news recommender systems. Although several recent surveys review significant challenges for deep learning-based news recommender systems, such as fairness, privacy-preserving, and responsibility, there is a lack of a systematic survey on Large Language Model (LLM)-based news recommender systems. In order to review different core methodologies and explore potential issues systematically, we categorize DLLM-based and GLLM-based news recommender systems under the umbrella of LLM-based news recommender systems. In this survey, we first overview the development of deep learning-based news recommender systems. Then, we review LLM-based news recommender systems based on three aspects: news-oriented modeling, user-oriented modeling, and prediction-oriented modeling. Next, we examine the challenges from various perspectives, including datasets, benchmarking tools, and methodologies. Furthermore, we conduct extensive experiments to analyze how large language model technologies affect the performance of different news recommender systems. Finally, we comprehensively explore the future directions for LLM-based news recommendations in the era of LLMs.