mlsanityData to 14 Oct 2026

A Survey on User Behavior Modeling in Recommender Systems

arXiv 2302.11087 ↗First posted 22 Feb 2023cs.IR cs.LGcited by 26

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Abstract (arXiv)

User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many recommendation tasks. In this paper, we attempt to provide a thorough survey of this research topic. We start by reviewing the research background of UBM. Then, we provide a systematic taxonomy of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. Within each direction, representative models and their strengths and weaknesses are comprehensively discussed. Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions. Finally, we summarize the survey and discuss the future prospects of this field.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 38 of its 64 references that are in the map. First posted on arXiv 22 Feb 2023.

Builds on

38 of 64 references.

References · 38

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018Self-Attentive Sequential Recommendationcited by 1,184
  3. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  4. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
  5. 2014Neural Turing Machinescited by 644
  6. 2017Deep Interest Network for Click-Through Rate Predictioncited by 544
  7. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  8. 2018Deep Interest Evolution Network for Click-Through Rate Predictioncited by 338
  9. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  10. 2017Neural Attentive Session-based Recommendationcited by 237
  11. 2020Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Predictioncited by 177
  12. 2017Recurrent Neural Networks with Top-k Gains for Session-based Recommendationscited by 165
26 more
  1. 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 130
  2. 2018A Simple Convolutional Generative Network for Next Item Recommendationcited by 120
  3. 2019Practice on Long Sequential User Behavior Modeling for Click-Through Rate Predictioncited by 118
  4. 2015Ask Me Anything: Dynamic Memory Networks for Natural Language Processingcited by 117
  5. 2019Sequential Recommender Systems: Challenges, Progress and Prospectscited by 115
  6. 2021Sequential Recommendation with Graph Neural Networkscited by 89
  7. 2016Improved Recurrent Neural Networks for Session-based Recommendationscited by 86
  8. 2017Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networkscited by 84
  9. 2019Deep Session Interest Network for Click-Through Rate Predictioncited by 64
  10. 2021End-to-End User Behavior Retrieval in Click-Through RatePrediction Modelcited by 45
  11. 2020User Behavior Retrieval for Click-Through Rate Predictioncited by 44
  12. 2019Lifelong Sequential Modeling with Personalized Memorization for User Response Predictioncited by 41
  13. 2021Non-invasive Self-attention for Side Information Fusion in Sequential Recommendationcited by 39
  14. 2021Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendationcited by 39
  15. 2021Graph Meta Network for Multi-Behavior Recommendationcited by 38
  16. 2022Contrastive Meta Learning with Behavior Multiplicity for Recommendationcited by 37
  17. 2021DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendationcited by 34
  18. 2021Deep Learning for Click-Through Rate Estimationcited by 33
  19. 2022Decoupled Side Information Fusion for Sequential Recommendationcited by 30
  20. 2021Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Networkcited by 28
  21. 2022Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Predictioncited by 24
  22. 2022CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attentioncited by 22
  23. 2020Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendationcited by 18
  24. 2021Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commercecited by 7
  25. 2021MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Predictioncited by 5
  26. 2022Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Predictioncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 10; 2026: 7.

26 papers.

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  1. 2024ContextGNN: Beyond Two-Tower Recommendation Systemscited by 19
  2. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  3. 2024A Survey on Self-Supervised Learning for Non-Sequential Tabular Datacited by 15
  4. 2024An Automatic Graph Construction Framework based on Large Language Models for Recommendationcited by 9
  5. 2024CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign Assessmentcited by 8
  6. 2024Navigating the Landscape of Hint Generation Research: From the Past to the Futurecited by 6
  7. 2026CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMscited by 4
  8. 2025Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendationscited by 2
  9. 2025Enhancing Time Awareness in Generative Recommendationcited by 2
  10. 2025From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendationcited by 2
  11. 2026RelayGR: Scaling Long-Sequence Generative Recommendation via Cross-Stage Relay-Race Inferencecited by 2
  12. 2024Personalized Negative Reservoir for Incremental Learning in Recommender Systemscited by 1
14 more
  1. 2024Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendationcited by 1
  2. 2025MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendationcited by 1
  3. 2025Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Taskscited by 1
  4. 2026Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detectioncited by 1
  5. 2026ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisorscited by 1
  6. 2024Financial Risk Assessment via Long-term Payment Behavior Sequence Foldingcited by 0
  7. 2025Game of Trust: How Trustworthy Does Your Blockchain Think You Are?cited by 0
  8. 2025A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendationcited by 0
  9. 2025Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modelingcited by 0
  10. 2025Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenderscited by 0
  11. 2025Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendationcited by 0
  12. 2026ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modelingcited by 0
  13. 2026SocialPersona: Benchmarking Personalized Profiling and Response with Multimodal Social-Media Contextcited by 0
  14. 2026SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Cachingcited by 0