A Survey on LLM-based News Recommender Systems

2025-02-13

AI Summary

A systematic survey of large language models (LLMs) for news recommendation remains absent. Method: We propose the first unified taxonomy for LLM-based news recommendation, categorizing approaches along three axes—news modeling, user modeling, and prediction modeling—distinguishing discriminative and generative paradigms. We further introduce a multidimensional empirical analysis framework integrating cross-modal representation learning, user interest generation modeling, and standardized benchmarking tools. Extensive experiments are conducted across mainstream datasets. Contribution/Results: Our analysis quantitatively reveals architecture-specific impacts of LLMs on click-through rate, recommendation diversity, and interpretability. We establish the first comprehensive survey framework for LLM-powered news recommendation, explicitly identifying key challenges and future directions toward trustworthy, efficient, and personalized LLM-based recommendation systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Addresses information overload with news recommenders
• Surveys LLM-based news recommender system methodologies
• Explores challenges and future directions in LLM-based recommendations

Innovation

• LLM-based news recommendation
• Discriminative and Generative LLMs
• Systematic survey methodologies