mlsanityData to 14 Oct 2026

SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation

arXiv 2405.00287 ↗First posted 1 May 2024cs.IR cs.AI cs.LGcited by 4

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Abstract (arXiv)

Graph-based collaborative filtering (CF) has emerged as a promising approach in recommender systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. SCONE generates dynamic augmented views and diverse hard negative samples via a unified stochastic sampling approach based on score-based generative models. Our extensive experiments on 6 benchmark datasets show that SCONE consistently outperforms state-of-the-art baselines. SCONE shows efficacy in addressing user sparsity and item popularity issues, while enhancing performance for both cold-start users and long-tail items. Furthermore, our approach improves the diversity of the recommendation and the uniformity of the representations. The code is available at https://github.com/jeongwhanchoi/SCONE.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 38 of its 57 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

38 of 57 references.

References · 38

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2020Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hyperspherecited by 1,405
  6. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
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  10. 2020Self-supervised Graph Learning for Recommendationcited by 305
  11. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  12. 2020Embedding-based Retrieval in Facebook Searchcited by 156
26 more
  1. 2022Self-Supervised Learning for Recommender Systems: A Surveycited by 95
  2. 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
  3. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  4. 2017IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Modelscited by 75
  5. 2023Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusioncited by 74
  6. 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
  7. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  8. 2022XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendationcited by 57
  9. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  10. 2021Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translationcited by 45
  11. 2023A Content-Driven Micro-Video Recommendation Dataset at Scalecited by 45
  12. 2023Diffusion Augmentation for Sequential Recommendationcited by 38
  13. 2022Blurring-Sharpening Process Models for Collaborative Filteringcited by 35
  14. 2020Simplify and Robustify Negative Sampling for Implicit Collaborative Filteringcited by 34
  15. 2021Socially-Aware Self-Supervised Tri-Training for Recommendationcited by 32
  16. 2023An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attentioncited by 28
  17. 2021LT-OCF: Learnable-Time ODE-based Collaborative Filteringcited by 21
  18. 2023Generative Contrastive Graph Learning for Recommendationcited by 20
  19. 2021Linear, or Non-Linear, That is the Question!cited by 19
  20. 2016A Generic Coordinate Descent Framework for Learning from Implicit Feedbackcited by 16
  21. 2023Contrastive Self-supervised Learning in Recommender Systems: A Surveycited by 14
  22. 2022MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filteringcited by 11
  23. 2023Graph Contrastive Learning with Generative Adversarial Networkcited by 8
  24. 2023RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendationcited by 3
  25. 2023Dimension Independent Mixup for Hard Negative Sample in Collaborative Filteringcited by 2
  26. 2024QoS-Aware Graph Contrastive Learning for Web Service Recommendationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024SVD-AE: Simple Autoencoders for Collaborative Filteringcited by 4
  2. 2025Continuous-time Discrete-space Diffusion Model for Recommendationcited by 3
  3. 2026Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systemscited by 1
  4. 2025TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity
  2. 2024Score-based Generative Diffusion Models for Social Recommendations
  3. 2024S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain
  4. 2025Hard Negative Sampling via Large Language Models for Recommendation
  5. 2025Towards A Tri-View Diffusion Framework for Recommendation
  6. 2024Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
  7. 2025On Negative-aware Preference Optimization for Recommendation
  8. 2025Graph-based Diffusion Model for Collaborative Filtering