mlsanityData to 14 Oct 2026

GraphFM: Graph Factorization Machines for Feature Interaction Modeling

arXiv 2105.11866 ↗First posted 25 May 2021cs.LG cs.AI cs.IRcited by 13

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Abstract (arXiv)

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

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 52 of its 75 references that are in the map. First posted on arXiv 25 May 2021.

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52 of 75 references.

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Citing papers per year: 2022: 4; 2023: 5; 2024: 1; 2025: 2; 2026: 1.

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  1. 2022Click-Through Rate Prediction in Online Advertising: A Literature Reviewcited by 31
  2. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  3. 2023Embedding in Recommender Systems: A Surveycited by 20
  4. 2023Causality-based CTR Prediction using Graph Neural Networkscited by 9
  5. 2023HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformercited by 8
  6. 2023FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Trainingcited by 4
  7. 2022Explicit Feature Interaction-aware Graph Neural Networkscited by 3
  8. 2022Boosting Factorization Machines via Saliency-Guided Mixupcited by 3
  9. 2024Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendationcited by 3
  10. 2022Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillationcited by 1
  11. 2025From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNscited by 0
  12. 2025DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendationcited by 0
1 more
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