GraphFM: Graph Factorization Machines for Feature Interaction Modeling

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 https://github.com/CRIPAC-DIG/GraphCTR}{https://github.com/CRIPAC-DIG/GraphCTR

The code and data are available at https://github.com/CRIPAC-DIG/GraphCTR

References in corpus (18)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Distilling the Knowledge in a Neural Network
• Semi-Supervised Classification with Graph Convolutional Networks
• Neural Graph Collaborative Filtering
• Spectral Networks and Locally Connected Networks on Graphs
• Session-based Recommendation with Graph Neural Networks
• Understanding deep learning requires rethinking generalization
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
• AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
• DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
• Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
• Differentiable Graph Module (DGM) for Graph Convolutional Networks
• Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
• $F{M}^2$: Field-matrixed Factorization Machines for Recommender Systems
• Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
• Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
• TFNet: Multi-Semantic Feature Interaction for CTR Prediction
• Graph Factorization Machines for Cross-Domain Recommendation