整合图神经网络与大模型，提升推荐系统对用户行为和文本的建模能力。
Graph Foundation Models for Recommendation: A Comprehensive Survey
- 融合GNN与LLM，利用用户-物品图结构与文本信息联合建模
- 通过图结构捕捉高阶关系，提升推荐准确率与可解释性
- 适合研究推荐系统架构、图模型与大模型融合的学者参考
推荐系统是导航海量在线信息的核心工具，深度学习技术持续推动其性能提升。图神经网络（GNN）擅长提取用户-物品交互中的高阶结构信息，而大语言模型（LLM）则在自然语言理解方面表现卓越。近年来，图基础模型（GFMs）兴起，通过结合GNN与LLM的优势，利用用户-物品图结构与文本理解能力，更高效地建模复杂的推荐问题。本文全面综述了基于图基础模型的推荐技术，提出清晰的分类体系，深入剖析方法细节，并指出关键挑战与未来方向。通过整合最新进展，为理解该领域的演进提供重要参考。
原文摘要 · Abstract (English)
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role in improving ranking accuracy. Among these, graph neural networks (GNNs) excel at extracting higher-order structural information, while large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted. Recent research has focused on graph foundation models (GFMs), which integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding. In this survey, we provide a comprehensive overview of GFM-based RS technologies by introducing a clear taxonomy of current approaches, diving into methodological details, and highlighting key challenges and future directions. By synthesizing recent advancements, we aim to offer valuable insights into the evolving landscape of GFM-based recommender systems.
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