GraphFM: Graph Factorization Machines for Feature Interaction Modelling
Published in Machine Intelligence Research, 2025
This work integrates graph structures into the Factorization Machines (FM) framework, modelling feature interactions from a topological perspective. By leveraging GNN-based aggregation, it explicitly captures high-order interactions while automatically filtering noise, addressing inherent limitations of traditional FMs.
Recommended citation: Shu Wu, Zekun Li, Yunyue Su, Zeyu Cui, Xiaoyu Zhang, Liang Wang. (2025). "GraphFM: Graph Factorization Machines for Feature Interaction Modelling." Machine Intelligence Research, 22(2), 239-253.
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