Large Language Models for Recommender Systems

Dear Colleagues,

Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence, offering unprecedented capabilities in natural language understanding, reasoning, and generative tasks. In parallel, recommender systems remain a cornerstone of digital platforms, guiding user decisions in domains such as e-commerce, media, education, and healthcare. The intersection of these two areas opens new opportunities for building highly personalized, context-aware, and conversational recommendation services. Unlike traditional recommenders that primarily rely on structured interaction data, LLM-powered recommenders can leverage unstructured content, dialogue history, and user intent expressed in natural language. This shift not only enhances user experience but also raises new research challenges in scalability, fairness, explainability, and evaluation.

This Special Issue aims at providing a dedicated venue for advancing research at the convergence of LLMs and recommender systems. We seek to explore how LLMs can enrich recommender pipelines—ranging from candidate generation and ranking to explanation and interactive recommendation—and how recommender system requirements can in turn shape the development of more efficient and trustworthy LLMs. This Special Issue aligns with the journal’s mission to showcase cutting-edge research in artificial intelligence, data-driven systems, and human-centered computing. Contributions will highlight both theoretical advances and practical deployments, fostering dialogue between the recommender systems and natural language processing communities.

We invite contributions on topics including, but not limited to, the following:

• Architectures and frameworks for integrating LLMs with traditional recommender pipelines.
• Prompt engineering, fine-tuning, and alignment strategies for recommendation tasks.
• LLMs for conversational and dialogue-based recommender systems.
• Multimodal recommendation leveraging LLMs (e.g., text, image, video, and audio).
• Scalability, efficiency, and resource optimization in LLM-powered recommendation.
• Fairness, bias mitigation, explainability, and transparency.
• Human–AI collaboration and user experience design.
• Benchmarks, evaluation methodologies, and reproducibility in LLM-based recommendation research.
• Real-world applications and case studies across industries such as retail, media, healthcare, and education.

In this Special Issue, we welcome both original research articles and comprehensive review papers that push the boundaries of knowledge in this timely and impactful area.

We look forward to receiving your contributions.

Dr. Yong Zheng
Dr. Peng Liu
Guest Editors

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• LLM
• recommender system
• prompt
• personalization