# A Survey on User Behavior Modeling in Recommender Systems

arXiv: https://arxiv.org/abs/2302.11087 · HTML: https://mlsanity.com/paper/2302.11087/

## 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 (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [A Simple Convolutional Generative Network for Next Item Recommendation](https://mlsanity.com/paper/1808.05163/) (2018)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://mlsanity.com/paper/1506.07285/) (2015)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Sequential Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2106.14226/) (2021)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://mlsanity.com/paper/2108.04468/) (2021)
- [User Behavior Retrieval for Click-Through Rate Prediction](https://mlsanity.com/paper/2005.14171/) (2020)
- [Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction](https://mlsanity.com/paper/1905.00758/) (2019)
- [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation](https://mlsanity.com/paper/2103.03578/) (2021)
- [Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.04000/) (2021)
- [Graph Meta Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.03969/) (2021)
- [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://mlsanity.com/paper/2202.08523/) (2022)
- [DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2105.03300/) (2021)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)

## Built on by (most cited first, 26 of 26)

- [ContextGNN: Beyond Two-Tower Recommendation Systems](https://mlsanity.com/paper/2411.19513/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [A Survey on Self-Supervised Learning for Non-Sequential Tabular Data](https://mlsanity.com/paper/2402.01204/) (2024)
- [An Automatic Graph Construction Framework based on Large Language Models for Recommendation](https://mlsanity.com/paper/2412.18241/) (2024)
- [CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign Assessment](https://mlsanity.com/paper/2407.21553/) (2024)
- [Navigating the Landscape of Hint Generation Research: From the Past to the Future](https://mlsanity.com/paper/2404.04728/) (2024)
- [CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMs](https://mlsanity.com/paper/2602.00742/) (2026)
- [Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations](https://mlsanity.com/paper/2508.08454/) (2025)
- [Enhancing Time Awareness in Generative Recommendation](https://mlsanity.com/paper/2509.13957/) (2025)
- [From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation](https://mlsanity.com/paper/2509.23649/) (2025)
- [RelayGR: Scaling Long-Sequence Generative Recommendation via Cross-Stage Relay-Race Inference](https://mlsanity.com/paper/2601.01712/) (2026)
- [Personalized Negative Reservoir for Incremental Learning in Recommender Systems](https://mlsanity.com/paper/2403.03993/) (2024)
- [Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2405.12473/) (2024)
- [MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation](https://mlsanity.com/paper/2508.14515/) (2025)
- [Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks](https://mlsanity.com/paper/2510.11903/) (2025)
- [Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection](https://mlsanity.com/paper/2606.24985/) (2026)
- [ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors](https://mlsanity.com/paper/2608.01204/) (2026)
- [Financial Risk Assessment via Long-term Payment Behavior Sequence Folding](https://mlsanity.com/paper/2411.15056/) (2024)
- [Game of Trust: How Trustworthy Does Your Blockchain Think You Are?](https://mlsanity.com/paper/2505.14551/) (2025)
- [A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2506.11563/) (2025)
- [Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling](https://mlsanity.com/paper/2508.14910/) (2025)
- [Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders](https://mlsanity.com/paper/2510.08930/) (2025)
- [Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendation](https://mlsanity.com/paper/2511.06077/) (2025)
- [ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling](https://mlsanity.com/paper/2606.24605/) (2026)
- [SocialPersona: Benchmarking Personalized Profiling and Response with Multimodal Social-Media Context](https://mlsanity.com/paper/2606.26654/) (2026)
- [SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching](https://mlsanity.com/paper/2609.08443/) (2026)

## 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.