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GraphFM: Graph Factorization Machines for Feature Interaction Modelling
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AI
DOI：10.1007/s11633-024-1505-5.png)
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Abstract
En 中文
Factorization machine (FM) is a prevalent approach to modelling pairwise (second-order) feature interactions when dealing with high-dimensional sparse data. However, on the one hand, FMs fail 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 the prediction accuracy. To solve these problems, we propose a novel approach, the graph factorization machine (GraphFM), which naturally represents features in the graph structure. In particular, we design a mechanism to select beneficial feature interactions and formulate them as edges between features. Then the proposed model, which integrates the interaction function of the FM into the feature aggregation strategy of the graph neural network (GNN), can model arbitrary-order feature interactions on graph-structured features by stacking layers. Experimental results on several real-world datasets demonstrate the rationality and effectiveness of our proposed approach. The code and data are available at https://github.com/CRIPAC-DIG/GraphCTR.
Keywords:
Feature interaction
factorization machines
graph neural network
recommender system
deep learning
Journal
IF:
8.7
Papers:
304
Citations:
882
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