# Deep Learning for Click-Through Rate Estimation

arXiv: https://arxiv.org/abs/2104.10584 · HTML: https://mlsanity.com/paper/2104.10584/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 67 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09433/) (2019)
- [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://mlsanity.com/paper/1601.02376/) (2016)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://mlsanity.com/paper/1806.03514/) (2018)
- [Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/1910.05552/) (2019)
- [AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.11235/) (2020)
- [AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations](https://mlsanity.com/paper/2002.11252/) (2020)
- [Higher-Order Factorization Machines](https://mlsanity.com/paper/1607.07195/) (2016)
- [Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data](https://mlsanity.com/paper/1807.00311/) (2018)
- [User Behavior Retrieval for Click-Through Rate Prediction](https://mlsanity.com/paper/2005.14171/) (2020)
- [Learnable Embedding Sizes for Recommender Systems](https://mlsanity.com/paper/2101.07577/) (2021)
- [Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction](https://mlsanity.com/paper/1905.00758/) (2019)

## Built on by (most cited first, 30 of 33)

- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2308.11131/) (2023)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction](https://mlsanity.com/paper/2310.09234/) (2023)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [Counterfactual Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.01391/) (2023)
- [CL4CTR: A Contrastive Learning Framework for CTR Prediction](https://mlsanity.com/paper/2212.00522/) (2022)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction](https://mlsanity.com/paper/2203.11014/) (2022)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction](https://mlsanity.com/paper/2310.19453/) (2023)
- [MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction](https://mlsanity.com/paper/2308.01737/) (2023)
- [Unlocking the Transferability of Tokens in Deep Models for Tabular Data](https://mlsanity.com/paper/2310.15149/) (2023)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads](https://mlsanity.com/paper/2302.01255/) (2023)
- [Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation](https://mlsanity.com/paper/2604.20858/) (2026)
- [COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation](https://mlsanity.com/paper/2306.05001/) (2023)
- [DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion](https://mlsanity.com/paper/2508.14500/) (2025)
- [IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu](https://mlsanity.com/paper/2603.01590/) (2026)
- [FINT: Field-aware INTeraction Neural Network For CTR Prediction](https://mlsanity.com/paper/2107.01999/) (2021)
- [AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation](https://mlsanity.com/paper/2308.06965/) (2023)
- [Reinforcement Learning from Statistical Feedback: the Journey from AB Testing to ANT Testing](https://mlsanity.com/paper/2311.14766/) (2023)
- [A Deep Behavior Path Matching Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2302.00302/) (2023)
- [DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction](https://mlsanity.com/paper/2305.04891/) (2023)
- [STUDY: Socially Aware Temporally Causal Decoder Recommender Systems](https://mlsanity.com/paper/2306.07946/) (2023)
- [EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through Rate Prediction](https://mlsanity.com/paper/2310.04482/) (2023)
- [An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model](https://mlsanity.com/paper/2411.15223/) (2024)
- [Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2501.13344/) (2025)
- [Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking](https://mlsanity.com/paper/2502.18478/) (2025)

## Abstract (arXiv)

Click-through rate (CTR) estimation plays as a core function module in various personalized online services, including online advertising, recommender systems, and web search etc. From 2015, the success of deep learning started to benefit CTR estimation performance and now deep CTR models have been widely applied in many industrial platforms. In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks. First, we take a review of the transfer from shallow to deep CTR models and explain why going deep is a necessary trend of development. Second, we concentrate on explicit feature interaction learning modules of deep CTR models. Then, as an important perspective on large platforms with abundant user histories, deep behavior models are discussed. Moreover, the recently emerged automated methods for deep CTR architecture design are presented. Finally, we summarize the survey and discuss the future prospects of this field.