mlsanityData to 14 Oct 2026

Outer Product-based Neural Collaborative Filtering

arXiv 1808.03912 ↗First posted 12 Aug 2018cs.IR cs.LG stat.MLcited by 41

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Abstract (arXiv)

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model. The experiment codes are available at: https://github.com/duxy-me/ConvNCF

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Aug 2018.

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Citing papers per year: 2021: 11; 2022: 17; 2023: 4; 2024: 3; 2025: 5; 2026: 1.

41 papers.

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  1. 2022Recommendation Unlearningcited by 80
  2. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  3. 2021Generate Natural Language Explanations for Recommendationcited by 23
  4. 2022FedRecAttack: Model Poisoning Attack to Federated Recommendationcited by 20
  5. 2021Deep Exploration for Recommendation Systemscited by 14
  6. 2022Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Samplingcited by 14
  7. 2022Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarioscited by 13
  8. 2022The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithmcited by 9
  9. 2024Improved Diversity-Promoting Collaborative Metric Learning for Recommendationcited by 9
  10. 2023Multi-annotator Deep Learning: A Probabilistic Framework for Classificationcited by 8
  11. 2021Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendationcited by 7
  12. 2021Multi-Sample based Contrastive Loss for Top-k Recommendationcited by 7
29 more
  1. 2024Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendationcited by 7
  2. 2021Local Collaborative Autoencoderscited by 5
  3. 2022Knowledge Sharing via Domain Adaptation in Customs Fraud Detectioncited by 5
  4. 2022FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddingscited by 4
  5. 2022Bilateral Self-unbiased Learning from Biased Implicit Feedbackcited by 3
  6. 2021Multi-target prediction for dummies using two-branch neural networkscited by 2
  7. 2022Collaborative Reflection-Augmented Autoencoder Network for Recommender Systemscited by 2
  8. 2023Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Predictioncited by 2
  9. 2021Dual-embedding based Neural Collaborative Filtering for Recommender Systemscited by 1
  10. 2022A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendationcited by 1
  11. 2022Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filteringcited by 1
  12. 2022Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Predictioncited by 1
  13. 2022InterMulti:Multi-view Multimodal Interactions with Text-dominated Hierarchical High-order Fusion for Emotion Analysiscited by 1
  14. 2023Automatic Feature Fairness in Recommendation via Adversariescited by 1
  15. 2025Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intentcited by 1
  16. 2025Personalized Recommendations via Active Utility-based Pairwise Samplingcited by 1
  17. 2021BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanismcited by 0
  18. 2021A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systemscited by 0
  19. 2021MultiHead MultiModal Deep Interest Recommendation Networkcited by 0
  20. 2022Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approachcited by 0
  21. 2022One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representationscited by 0
  22. 2022Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systemscited by 0
  23. 2022EffMulti: Efficiently Modeling Complex Multimodal Interactions for Emotion Analysiscited by 0
  24. 2023Adaptive spectral graph wavelets for collaborative filteringcited by 0
  25. 2024Measuring the Predictability of Recommender Systems using Structural Complexity Metricscited by 0
  26. 2025Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systemscited by 0
  27. 2025Parameter-Efficient Single Collaborative Branch for Recommendationcited by 0
  28. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  29. 2026HandMCM: Multi-modal Point Cloud-based Correspondence State Space Model for 3D Hand Pose Estimationcited by 0