# User Behavior Retrieval for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2005.14171 · HTML: https://mlsanity.com/paper/2005.14171/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 30 of 44)

- [Diffusion Models for Reinforcement Learning: A Survey](https://mlsanity.com/paper/2311.01223/) (2023)
- [ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2308.11131/) (2023)
- [TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023](https://mlsanity.com/paper/2307.14338/) (2023)
- [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://mlsanity.com/paper/2108.04468/) (2021)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
- [TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou](https://mlsanity.com/paper/2407.16357/) (2024)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://mlsanity.com/paper/2205.10249/) (2022)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [CL4CTR: A Contrastive Learning Framework for CTR Prediction](https://mlsanity.com/paper/2212.00522/) (2022)
- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction](https://mlsanity.com/paper/2308.01737/) (2023)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation](https://mlsanity.com/paper/2506.02267/) (2025)
- [Learning Enhanced Representations for Tabular Data via Neighborhood Propagation](https://mlsanity.com/paper/2206.06587/) (2022)
- [DIWIFT: Discovering Instance-wise Influential Features for Tabular Data](https://mlsanity.com/paper/2207.02773/) (2022)
- [NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams](https://mlsanity.com/paper/2312.02473/) (2023)
- [MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling](https://mlsanity.com/paper/2512.07216/) (2025)
- [Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](https://mlsanity.com/paper/2308.05996/) (2023)
- [Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences](https://mlsanity.com/paper/2208.04022/) (2022)
- [Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/2209.12212/) (2022)
- [Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction](https://mlsanity.com/paper/2308.15703/) (2023)
- [AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling](https://mlsanity.com/paper/2210.15154/) (2022)
- [ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer](https://mlsanity.com/paper/2306.14392/) (2023)
- [DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation](https://mlsanity.com/paper/2406.00011/) (2024)
- [Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation](https://mlsanity.com/paper/2301.10443/) (2023)
- [Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems](https://mlsanity.com/paper/2401.04408/) (2024)
- [Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System](https://mlsanity.com/paper/2404.15678/) (2024)
- [Retrieval-Oriented Knowledge for Click-Through Rate Prediction](https://mlsanity.com/paper/2404.18304/) (2024)
- [MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation](https://mlsanity.com/paper/2508.14515/) (2025)

## Abstract (arXiv)

Click-through rate (CTR) prediction plays a key role in modern online personalization services. In practice, it is necessary to capture user's drifting interests by modeling sequential user behaviors to build an accurate CTR prediction model. However, as the users accumulate more and more behavioral data on the platforms, it becomes non-trivial for the sequential models to make use of the whole behavior history of each user. First, directly feeding the long behavior sequence will make online inference time and system load infeasible. Second, there is much noise in such long histories to fail the sequential model learning. The current industrial solutions mainly truncate the sequences and just feed recent behaviors to the prediction model, which leads to a problem that sequential patterns such as periodicity or long-term dependency are not embedded in the recent several behaviors but in far back history. To tackle these issues, in this paper we consider it from the data perspective instead of just designing more sophisticated yet complicated models and propose User Behavior Retrieval for CTR prediction (UBR4CTR) framework. In UBR4CTR, the most relevant and appropriate user behaviors will be firstly retrieved from the entire user history sequence using a learnable search method. These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones. It is highly feasible to deploy UBR4CTR into industrial model pipeline with low cost. Experiments on three real-world large-scale datasets demonstrate the superiority and efficacy of our proposed framework and models.