GraphFM: Graph Factorization Machines for Feature Interaction Modeling

• URL: http://arxiv.org/abs/2105.11866v4
• Date: Mon, 1 Apr 2024 03:36:20 GMT
• Title: GraphFM: Graph Factorization Machines for Feature Interaction Modeling
• Authors: Shu Wu, Zekun Li, Yunyue Su, Zeyu Cui, Xiaoyu Zhang, Liang Wang,
• Abstract summary: We propose a novel approach, Graph Factorization Machine (GraphFM), by naturally representing features in the graph structure. In particular, we design a mechanism to select the beneficial feature interactions and formulate them as edges between features. The proposed model integrates the interaction function of FM into the feature aggregation strategy of Graph Neural Network (GNN)
• Score: 27.307086868266012
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Factorization machine (FM) is a prevalent approach to modeling pairwise (second-order) feature interactions when dealing with high-dimensional sparse data. However, on the one hand, FM fails to capture higher-order feature interactions suffering from combinatorial expansion. On the other hand, taking into account interactions between every pair of features may introduce noise and degrade prediction accuracy. To solve the problems, we propose a novel approach, Graph Factorization Machine (GraphFM), by naturally representing features in the graph structure. In particular, we design a mechanism to select the beneficial feature interactions and formulate them as edges between features. Then the proposed model, which integrates the interaction function of FM into the feature aggregation strategy of Graph Neural Network (GNN), can model arbitrary-order feature interactions on the graph-structured features by stacking layers. Experimental results on several real-world datasets have demonstrated the rationality and effectiveness of our proposed approach. The code and data are available at \href{https://github.com/CRIPAC-DIG/GraphCTR}{https://github.com/CRIPAC-DIG/GraphCTR}.

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