Graph Foundation Models for Recommendation: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910830238367744 |
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| author | Wu, Bin Wang, Yihang Zeng, Yuanhao Liu, Jiawei Zhao, Jiashu Yang, Cheng Li, Yawen Xia, Long Yin, Dawei Shi, Chuan |
| author_facet | Wu, Bin Wang, Yihang Zeng, Yuanhao Liu, Jiawei Zhao, Jiashu Yang, Cheng Li, Yawen Xia, Long Yin, Dawei Shi, Chuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08346 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Foundation Models for Recommendation: A Comprehensive Survey Wu, Bin Wang, Yihang Zeng, Yuanhao Liu, Jiawei Zhao, Jiashu Yang, Cheng Li, Yawen Xia, Long Yin, Dawei Shi, Chuan Information Retrieval Artificial Intelligence Machine Learning 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. |
| title | Graph Foundation Models for Recommendation: A Comprehensive Survey |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.08346 |