mlsanityData to 7 Oct 2026

S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

arXiv 2008.07873 ↗First posted 18 Aug 2020cs.IR cs.LGcited by 311

Not on the map. Why?

Abstract (arXiv)

Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation. To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture. The main idea of our approach is to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence by utilizing the mutual information maximization (MIM) principle. MIM provides a unified way to characterize the correlation between different types of data, which is particularly suitable in our scenario. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available. Besides, we extend our self-supervised learning method to other recommendation models, which also improve their performance.

Key facts. Cited by 311 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Aug 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 38; 2023: 66; 2024: 69; 2025: 76; 2026: 48.

311 papers.

Sort:

By area:

  1. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  2. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  3. 2023Recommender Systems with Generative Retrievalcited by 191
  4. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 184
  5. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  6. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  7. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  8. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 120
  9. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  10. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  11. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  12. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 101
38 more
  1. 2022Filter-enhanced MLP is All You Need for Sequential Recommendationcited by 96
  2. 2022Intent Contrastive Learning for Sequential Recommendationcited by 90
  3. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  4. 2021Contrastive Self-supervised Sequential Recommendation with Robust Augmentationcited by 56
  5. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  6. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 50
  7. 2021Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformercited by 45
  8. 2023VIP5: Towards Multimodal Foundation Models for Recommendationcited by 45
  9. 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 45
  10. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  11. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  12. 2022CORE: Simple and Effective Session-based Recommendation within Consistent Representation Spacecited by 37
  13. 2023Diffusion Augmentation for Sequential Recommendationcited by 37
  14. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  15. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  16. 2021Double-Scale Self-Supervised Hypergraph Learning for Group Recommendationcited by 32
  17. 2022Decoupled Side Information Fusion for Sequential Recommendationcited by 30
  18. 2023MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendationcited by 30
  19. 2023gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Samplingcited by 29
  20. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 29
  21. 2023An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attentioncited by 28
  22. 2023A Survey on User Behavior Modeling in Recommender Systemscited by 26
  23. 2023AutoMLP: Automated MLP for Sequential Recommendationscited by 25
  24. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
  25. 2022C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender Systemcited by 24
  26. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  27. 2023Conditional Denoising Diffusion for Sequential Recommendationcited by 23
  28. 2024CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendationcited by 23
  29. 2021Lightweight Self-Attentive Sequential Recommendationcited by 22
  30. 2022CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attentioncited by 22
  31. 2022A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendationcited by 22
  32. 2023Disentangled Contrastive Collaborative Filteringcited by 22
  33. 2023Knowledge Graph Self-Supervised Rationalization for Recommendationcited by 22
  34. 2022Multi-view Multi-behavior Contrastive Learning in Recommendationcited by 21
  35. 2024LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Applicationcited by 21
  36. 2024RDRec: Rationale Distillation for LLM-based Recommendationcited by 21
  37. 2022Multi-Behavior Sequential Recommendation with Temporal Graph Transformercited by 20
  38. 2022ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendationcited by 19

50 most cited of 311 All 311 (JSON)