mlsanityData to 7 Oct 2026

Neural Collaborative Filtering

arXiv 1708.05031 ↗First posted 16 Aug 2017cs.IRcited by 1,108

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Cited by 27 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

Key facts. Cited by 1,108 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Aug 2017.

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Citing papers per year: 2020: 1; 2021: 201; 2022: 196; 2023: 219; 2024: 196; 2025: 187; 2026: 108.

1,108 papers.

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  12. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
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  5. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
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  12. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
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  15. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
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  18. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  19. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  20. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  21. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  22. 2023GenRec: Large Language Model for Generative Recommendationcited by 62
  23. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
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  26. 2021Membership Inference Attacks Against Recommender Systemscited by 52
  27. 2021How Powerful is Graph Convolution for Recommendation?cited by 50
  28. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  29. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  30. 2022Non-IID Transfer Learning on Graphscited by 43
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  35. 2025A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applicationscited by 40
  36. 2021Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertisingcited by 39
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