This survey paper provides a comprehensive overview of Graph Foundation Models (GFMs) applied to recommender systems. It details how GFMs combine the strengths of graph neural networks (GNNs) for structural information and large language models (LLMs) for textual understanding. The paper introduces a taxonomy of current GFM approaches in recommendation, discusses their methodologies, and outlines key challenges and future research directions. AI
IMPACT Provides a structured overview of how advanced AI models are being applied to improve information retrieval and personalization.
RANK_REASON The item is a survey paper on arXiv detailing a research area. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Bin Wu
- deep learning
- Graph Foundation Models
- graph neural networks
- Hugging Face
- large language models
- Recommender Systems
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