mlsanityData to 14 Oct 2026

Collaborative Diffusion Model for Recommender System

arXiv 2501.18997 ↗First posted 31 Jan 2025cs.IRcited by 1

On the map: Diffusion models → Diffusion recommenders

Abstract (arXiv)

Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities. However, existing DR face two central limitations: (i) a trade-off between enhancing generative capacity via noise injection and retaining the loss of personalized information. (ii) the underutilization of rich item-side information. To address these challenges, we present a Collaborative Diffusion model for Recommender System (CDiff4Rec). Specifically, CDiff4Rec generates pseudo-users from item features and leverages collaborative signals from both real and pseudo personalized neighbors identified through behavioral similarity, thereby effectively reconstructing nuanced user preferences. Experimental results on three public datasets show that CDiff4Rec outperforms competitors by effectively mitigating the loss of personalized information through the integration of item content and collaborative signals.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Diffusion recommenders, one of 14 directions in Diffusion models. Builds on 15 of its 29 references that are in the map. First posted on arXiv 31 Jan 2025.

Builds on

15 of 29 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Diffusion Augmentation for Sequential Recommendation22 Sep
  2. 2023DiffuRec: A Diffusion Model for Sequential Recommendation3 Apr

Other references · 13

  1. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  2. 2017Neural Collaborative Filteringcited by 1,111
  3. 2019Neural Graph Collaborative Filteringcited by 661
  4. 2022Understanding Diffusion Models: A Unified Perspectivecited by 340
  5. 2018Variational Autoencoders for Collaborative Filteringcited by 310
  6. 2019Embarrassingly Shallow Autoencoders for Sparse Datacited by 94
  7. 2023Diffusion Recommender Modelcited by 88
  8. 2017IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Modelscited by 75
  9. 2023Collaborative Large Language Model for Recommender Systemscited by 64
  10. 2022Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systemscited by 9
  11. 2024Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category Preferencescited by 4
  12. 2018A Hybrid Variational Autoencoder for Collaborative Filteringcited by 2
1 more
  1. 2020Conditioned Variational Autoencoder for top-N item recommendationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Survey on Diffusion Models for Recommender Systems
  2. 2024Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
  3. 2026Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation
  4. 2025Continuous-time Discrete-space Diffusion Model for Recommendation
  5. 2023Graph Signal Diffusion Model for Collaborative Filtering
  6. 2025Diffusion Models in Recommendation Systems: A Survey
  7. 2026Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systems
  8. 2025Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation