Graph Foundation Models for Recommendation: A Comprehensive Survey

TL;DR

This survey reviews the emerging field of graph foundation models that combine graph neural networks and large language models to enhance recommender systems, highlighting methodologies, challenges, and future prospects.

Contribution

It provides a comprehensive taxonomy and analysis of GFM-based recommender systems, summarizing recent advancements and identifying key challenges and future directions.

Findings

GFM approaches effectively integrate GNNs and LLMs for recommendation tasks.

Recent GFM models demonstrate improved accuracy over traditional methods.

The survey highlights open challenges and potential research directions in GFM-based RS.

Abstract

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.