mlsanityData to 14 Oct 2026

Disentangled Contrastive Learning for Social Recommendation

arXiv 2208.08723 ↗First posted 18 Aug 2022cs.IR cs.AIcited by 27

Not on the map. Why?

Abstract (arXiv)

Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the user-item interactions (collaborative domain) and social relations (social domain). However, such an approach may fail to model the users heterogeneous behavior patterns in two domains, impairing the expressiveness of user representations. In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec. More specifically, we propose to learn disentangled users representations from the item and social domains. Moreover, disentangled contrastive learning is designed to perform knowledge transfer between disentangled users representations for social recommendations. Comprehensive experiments on various real-world datasets demonstrate the superiority of our proposed model.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 18 Aug 2022.

Builds on

32 of 50 references.

References · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  6. 2020Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hyperspherecited by 1,405
  7. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  8. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  9. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  10. 2017Neural Collaborative Filteringcited by 1,111
  11. 2019Graph Neural Networks for Social Recommendationcited by 714
  12. 2019Neural Graph Collaborative Filteringcited by 661
20 more
  1. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  2. 2020Self-supervised Graph Learning for Recommendationcited by 305
  3. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  4. 2020Self-supervised Learning on Graphs: Deep Insights and New Directioncited by 108
  5. 2021Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendationcited by 101
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2019A Neural Influence Diffusion Model for Social Recommendationcited by 70
  8. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  9. 2020AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendationscited by 47
  10. 2020DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendationcited by 46
  11. 2020Pre-Training Graph Neural Networks for Cold-Start Users and Items Representationcited by 42
  12. 2019Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systemscited by 41
  13. 2021Prototypical Graph Contrastive Learningcited by 37
  14. 2019Deep Social Collaborative Filteringcited by 32
  15. 2021Socially-Aware Self-Supervised Tri-Training for Recommendationcited by 32
  16. 2020Attacking Black-box Recommendations via Copying Cross-domain User Profilescited by 31
  17. 2021AutoLoss: Automated Loss Function Search in Recommendationscited by 31
  18. 2022Knowledge-enhanced Black-box Attacks for Recommendationscited by 26
  19. 2019Deep Adversarial Social Recommendationcited by 20
  20. 2021Jointly Attacking Graph Neural Network and its Explanationscited by 18

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 11; 2025: 7; 2026: 2.

27 papers.

Sort:

By area:

  1. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  2. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  3. 2025RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMscited by 61
  4. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  5. 2024Score-based Generative Diffusion Models for Social Recommendationscited by 23
  6. 2024Graph Unlearning with Efficient Partial Retrainingcited by 20
  7. 2024RecDiff: Diffusion Model for Social Recommendationcited by 20
  8. 2023GraphPro: Graph Pre-training and Prompt Learning for Recommendationcited by 17
  9. 2025Cost-Aware Contrastive Routing for LLMscited by 16
  10. 2023Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selectioncited by 14
  11. 2024Adapt2Reward: Adapting Video-Language Models to Generalizable Robotic Rewards via Failure Promptscited by 10
  12. 2023Multi-agent Attacks for Black-box Social Recommendationscited by 9
15 more
  1. 2024A Comprehensive Survey on Self-Supervised Learning for Recommendationcited by 6
  2. 2024Backdoor Graph Condensationcited by 5
  3. 2025SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extractioncited by 5
  4. 2024DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosiscited by 4
  5. 2025Multi-Behavior Recommender Systems: A Surveycited by 4
  6. 2026Seeing Farther and Smarter: Value-Guided Multi-Path Reflection for VLM Policy Optimizationcited by 4
  7. 2025SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Modelscited by 3
  8. 2024Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Biascited by 2
  9. 2023Multi-Domain Learning From Insufficient Annotationscited by 1
  10. 2023IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendationcited by 1
  11. 2024Learning Social Graph for Inactive User Recommendationcited by 1
  12. 2025Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendationscited by 1
  13. 2024MixRec: Heterogeneous Graph Collaborative Filteringcited by 0
  14. 2025Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusioncited by 0
  15. 2026TrajMind: Chaining Role-Specialized LoRAs for Fast-and-Slow Collective Trajectory Anomaly Diagnosiscited by 0