综述大模型如何提升多模态推荐系统性能
A Survey on Large Language Models in Multimodal Recommender Systems
- 按提示工程、微调等方法分类整合大模型应用
- 梳理了主流数据集与评估指标，揭示技术演进路径
- 适合想快速了解该领域进展的研究者参考
多模态推荐系统（MRS）融合文本、图像和结构化信息等异构数据以提升推荐效果。大语言模型（LLMs）的出现为MRS带来新机遇，支持语义推理、上下文学习和动态输入处理。相较于早期预训练语言模型（PLMs），LLMs具备更强的灵活性与泛化能力，但也面临可扩展性和模型访问性挑战。本综述全面回顾了LLMs与MRS交叉领域的最新研究，聚焦提示策略、微调方法与数据适配技术。提出新型分类体系，归纳可迁移技术，总结评估指标与数据集，并指明未来方向。旨在厘清LLMs在多模态推荐中的新兴角色，推动该快速演进领域的发展。
原文摘要 · Abstract (English)
Multimodal recommender systems (MRS) integrate heterogeneous user and item data, such as text, images, and structured information, to enhance recommendation performance. The emergence of large language models (LLMs) introduces new opportunities for MRS by enabling semantic reasoning, in-context learning, and dynamic input handling. Compared to earlier pre-trained language models (PLMs), LLMs offer greater flexibility and generalisation capabilities but also introduce challenges related to scalability and model accessibility. This survey presents a comprehensive review of recent work at the intersection of LLMs and MRS, focusing on prompting strategies, fine-tuning methods, and data adaptation techniques. We propose a novel taxonomy to characterise integration patterns, identify transferable techniques from related recommendation domains, provide an overview of evaluation metrics and datasets, and point to possible future directions. We aim to clarify the emerging role of LLMs in multimodal recommendation and support future research in this rapidly evolving field.
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