mlsanityData to 7 Oct 2026

Self-Attentive Sequential Recommendation

arXiv 1808.09781 ↗First posted 20 Aug 2018cs.IR cs.LGcited by 1,169

Not on the map. Why?

Cited by 64 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently. To capture such patterns, two approaches have proliferated: Markov Chains (MCs) and Recurrent Neural Networks (RNNs). Markov Chains assume that a user's next action can be predicted on the basis of just their last (or last few) actions, while RNNs in principle allow for longer-term semantics to be uncovered. Generally speaking, MC-based methods perform best in extremely sparse datasets, where model parsimony is critical, while RNNs perform better in denser datasets where higher model complexity is affordable. The goal of our work is to balance these two goals, by proposing a self-attention based sequential model (SASRec) that allows us to capture long-term semantics (like an RNN), but, using an attention mechanism, makes its predictions based on relatively few actions (like an MC). At each time step, SASRec seeks to identify which items are `relevant' from a user's action history, and use them to predict the next item. Extensive empirical studies show that our method outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets. Moreover, the model is an order of magnitude more efficient than comparable CNN/RNN-based models. Visualizations on attention weights also show how our model adaptively handles datasets with various density, and uncovers meaningful patterns in activity sequences.

Key facts. Cited by 1,169 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2018.

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Citing papers per year: 2021: 74; 2022: 118; 2023: 165; 2024: 204; 2025: 294; 2026: 314.

1,169 papers.

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  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. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  7. 2023User Behavior Simulation with Large Language Model based Agentscited by 136
  8. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 130
  9. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  10. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  11. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 120
  12. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
38 more
  1. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  2. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  3. 2021STAN: Spatio-Temporal Attention Network for Next Location Recommendationcited by 102
  4. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  5. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 101
  6. 2022Filter-enhanced MLP is All You Need for Sequential Recommendationcited by 96
  7. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  8. 2023DiffuRec: A Diffusion Model for Sequential Recommendationcited by 91
  9. 2022Intent Contrastive Learning for Sequential Recommendationcited by 90
  10. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  11. 2023On the Possibilities of AI-Generated Text Detectioncited by 88
  12. 2023On Generative Agents in Recommendationcited by 88
  13. 2023Data Distillation: A Surveycited by 86
  14. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 76
  15. 2023Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusioncited by 72
  16. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
  17. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  18. 2024Large Language Models Empowered Personalized Web Agentscited by 64
  19. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  20. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  21. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  22. 2022PinnerFormer: Sequence Modeling for User Representation at Pinterestcited by 57
  23. 2021Contrastive Self-supervised Sequential Recommendation with Robust Augmentationcited by 56
  24. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  25. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
  26. 2023Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendationscited by 50
  27. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 50
  28. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  29. 2025Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendationcited by 50
  30. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  31. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  32. 2025Reinforced Latent Reasoning for LLM-based Recommendationcited by 46
  33. 2021Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformercited by 45
  34. 2021Zero-Shot Recommender Systemscited by 45
  35. 2021End-to-End User Behavior Retrieval in Click-Through RatePrediction Modelcited by 45
  36. 2023VIP5: Towards Multimodal Foundation Models for Recommendationcited by 45
  37. 2025Agentic Web: Weaving the Next Web with AI Agentscited by 43
  38. 2022Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendationcited by 42

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