# ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

arXiv: https://arxiv.org/abs/1711.06632 · HTML: https://mlsanity.com/paper/1711.06632/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 30 of 45)

- [Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding](https://mlsanity.com/paper/2207.14539/) (2022)
- [Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting](https://mlsanity.com/paper/2111.13684/) (2021)
- [Multi-view Multi-behavior Contrastive Learning in Recommendation](https://mlsanity.com/paper/2203.10576/) (2022)
- [Knowledge Enhancement for Contrastive Multi-Behavior Recommendation](https://mlsanity.com/paper/2301.05403/) (2023)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation](https://mlsanity.com/paper/2203.12267/) (2022)
- [Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations](https://mlsanity.com/paper/2107.05235/) (2021)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)
- [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](https://mlsanity.com/paper/2110.11072/) (2021)
- [MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems](https://mlsanity.com/paper/2402.14230/) (2024)
- [Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](https://mlsanity.com/paper/2308.05996/) (2023)
- [player2vec: A Language Modeling Approach to Understand Player Behavior in Games](https://mlsanity.com/paper/2404.04234/) (2024)
- [TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation](https://mlsanity.com/paper/2103.08971/) (2021)
- [Dynamic Sequential Graph Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2109.12541/) (2021)
- [Integrating Item Relevance in Training Loss for Sequential Recommender Systems](https://mlsanity.com/paper/2305.10824/) (2023)
- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences](https://mlsanity.com/paper/2208.04022/) (2022)
- [Triangle Graph Interest Network for Click-through Rate Prediction](https://mlsanity.com/paper/2202.02698/) (2022)
- [Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model](https://mlsanity.com/paper/2207.06652/) (2022)
- [AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking](https://mlsanity.com/paper/2310.09706/) (2023)
- [Denoising Time Cycle Modeling for Recommendation](https://mlsanity.com/paper/2402.02718/) (2024)
- [Modeling High-order Interactions across Multi-interests for Micro-video Recommendation](https://mlsanity.com/paper/2104.00305/) (2021)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction](https://mlsanity.com/paper/2110.01794/) (2021)
- [Learning to Learn a Cold-start Sequential Recommender](https://mlsanity.com/paper/2110.09083/) (2021)
- [Dynamic Parameterized Network for CTR Prediction](https://mlsanity.com/paper/2111.04983/) (2021)
- [Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis](https://mlsanity.com/paper/2207.11776/) (2022)
- [Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series](https://mlsanity.com/paper/2411.01418/) (2024)
- [C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation](https://mlsanity.com/paper/2506.13021/) (2025)
- [Abacus: Self-Supervised Event Counting-Aligned Distributional Pretraining for Sequential User Modeling](https://mlsanity.com/paper/2512.16581/) (2025)

## Abstract (arXiv)

A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct. Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications. However, this can only preserve very limited information, or aggregated memories of a person. When a downstream application requires to facilitate the modeled user features, it may lose the integrity of the specific highly correlated behavior of the user, and introduce noises derived from unrelated behaviors. This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks. Heterogeneous user behaviors are considered in our model that we project all types of behaviors into multiple latent semantic spaces, where influence can be made among the behaviors via self-attention. Downstream applications then can use the user behavior vectors via vanilla attention. Experiments show that ATRank can achieve better performance and faster training process. We further explore ATRank to use one unified model to predict different types of user behaviors at the same time, showing a comparable performance with the highly optimized individual models.