mlsanityData to 14 Oct 2026

Graph Foundation Models for Recommendation: A Comprehensive Survey

arXiv 2502.08346 ↗First posted 12 Feb 2025cs.IR cs.AI cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 64 of its 107 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

64 of 107 references.

References · 64

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38 more
  1. 2023A Survey on Large Language Models for Recommendationcited by 379
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  17. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
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50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Towards Graph Foundation Models: A Transferability Perspectivecited by 7
  2. 2026Out-of-Distribution Generalization in Graph Foundation Modelscited by 0
  3. 2026Can Graph Foundation Models Generalize Over Architecture?cited by 0
  4. 2026Riemannian Graph Tokenizer for Structural Knowledge Transfercited by 0