mlsanityData to 14 Oct 2026

Behavior Sequence Transformer for E-commerce Recommendation in Alibaba

arXiv 1905.06874 ↗First posted 15 May 2019cs.IR cs.LGcited by 130

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Abstract (arXiv)

Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. However, most of these works just concatenate different features, ignoring the sequential nature of users' behaviors. In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying users' behavior sequences for recommendation in Alibaba. Experimental results demonstrate the superiority of the proposed model, which is then deployed online at Taobao and obtain significant improvements in online Click-Through-Rate (CTR) comparing to two baselines.

Key facts. Cited by 130 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2019.

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Citing papers per year: 2021: 14; 2022: 25; 2023: 32; 2024: 18; 2025: 23; 2026: 18.

130 papers.

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  1. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 188
  2. 2022A Review of Sparse Expert Models in Deep Learningcited by 148
  3. 2023A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexitycited by 77
  4. 2023Max-Margin Token Selection in Attention Mechanismcited by 58
  5. 2022PinnerFormer: Sequence Modeling for User Representation at Pinterestcited by 57
  6. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
  7. 2025Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendationcited by 51
  8. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  9. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  10. 2021Deep Learning for Click-Through Rate Estimationcited by 33
  11. 2022ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputscited by 32
  12. 2021Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problemcited by 31
38 more
  1. 2021EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gatecited by 30
  2. 2024TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishoucited by 30
  3. 2025PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendationscited by 30
  4. 2021TabGNN: Multiplex Graph Neural Network for Tabular Data Predictioncited by 26
  5. 2021Scaling Law for Recommendation Models: Towards General-purpose User Representationscited by 26
  6. 2023A Survey on User Behavior Modeling in Recommender Systemscited by 26
  7. 2022Rethinking Personalized Ranking at Pinterest: An End-to-End Approachcited by 16
  8. 2023MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systemscited by 14
  9. 2023FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Datacited by 14
  10. 2024What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encodingcited by 14
  11. 2024Ads Recommendation in a Collapsed and Entangled Worldcited by 13
  12. 2022PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendationcited by 12
  13. 2024LiRank: Industrial Large Scale Ranking Models at LinkedIncited by 12
  14. 2022KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Pluggingcited by 11
  15. 2023Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learningcited by 11
  16. 2023Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detectioncited by 11
  17. 2025Hierarchical LoRA MoE for Efficient CTR Model Scalingcited by 10
  18. 2022RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructurecited by 9
  19. 2023Temporal Interest Network for User Response Predictioncited by 9
  20. 2025TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendationcited by 9
  21. 2022Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Searchcited by 8
  22. 2023BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Predictioncited by 8
  23. 2024LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detectioncited by 8
  24. 2024InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Predictioncited by 8
  25. 2021PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusioncited by 7
  26. 2021Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commercecited by 7
  27. 2022NASRec: Weight Sharing Neural Architecture Search for Recommender Systemscited by 7
  28. 2022Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendationcited by 7
  29. 2023Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networkscited by 7
  30. 2024Long-Sequence Recommendation Models Need Decoupled Embeddingscited by 7
  31. 2025On the Convergence of Gradient Descent on Learning Transformers with Residual Connectionscited by 6
  32. 2021PreSizE: Predicting Size in E-Commerce using Transformerscited by 5
  33. 2023Deep Task-specific Bottom Representation Network for Multi-Task Recommendationcited by 5
  34. 2024Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Maskcited by 5
  35. 2024Efficient user history modeling with amortized inference for deep learning recommendation modelscited by 5
  36. 2026LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendationcited by 5
  37. 2022CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPUcited by 4
  38. 2022PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actionscited by 4

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