mlsanityData to 7 Oct 2026

Deep Learning for Click-Through Rate Estimation

arXiv 2104.10584 ↗First posted 21 Apr 2021cs.IR cs.LGcited by 33

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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.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 37 of its 67 references that are in the map. First posted on arXiv 21 Apr 2021.

Builds on

37 of 67 references.

References · 37

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  6. 2016Wide & Deep Learning for Recommender Systemscited by 745
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  8. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
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  10. 2018Deep Interest Evolution Network for Click-Through Rate Predictioncited by 336
  11. 2018xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemscited by 277
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25 more
  1. 2020Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Predictioncited by 176
  2. 2016Product-based Neural Networks for User Response Predictioncited by 145
  3. 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 128
  4. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
  5. 2020Neural Collaborative Filtering vs. Matrix Factorization Revisitedcited by 123
  6. 2019Practice on Long Sequential User Behavior Modeling for Click-Through Rate Predictioncited by 118
  7. 2019FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Predictioncited by 83
  8. 2016Deep Learning over Multi-field Categorical Data: A Case Study on User Response Predictioncited by 67
  9. 2019Deep Session Interest Network for Click-Through Rate Predictioncited by 64
  10. 2018Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertisingcited by 59
  11. 2019Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predictioncited by 54
  12. 2020AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Predictioncited by 52
  13. 2020AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendationscited by 47
  14. 2016Higher-Order Factorization Machinescited by 45
  15. 2018Product-based Neural Networks for User Response Prediction over Multi-field Categorical Datacited by 44
  16. 2020User Behavior Retrieval for Click-Through Rate Predictioncited by 44
  17. 2021Learnable Embedding Sizes for Recommender Systemscited by 42
  18. 2019Lifelong Sequential Modeling with Personalized Memorization for User Response Predictioncited by 41
  19. 2019Neural Input Search for Large Scale Recommendation Modelscited by 39
  20. 2020Towards Automated Neural Interaction Discovery for Click-Through Rate Predictioncited by 37
  21. 2019Feature Generation by Convolutional Neural Network for Click-Through Rate Predictioncited by 26
  22. 2019Efficient Neural Interaction Function Search for Collaborative Filteringcited by 16
  23. 2020Differentiable Neural Input Search for Recommender Systemscited by 16
  24. 2020Memory-efficient Embedding for Recommendationscited by 15
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 16; 2024: 2; 2025: 6; 2026: 3.

33 papers.

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  1. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  2. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 76
  3. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 36
  4. 2022Click-Through Rate Prediction in Online Advertising: A Literature Reviewcited by 31
  5. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  6. 2023A Survey on User Behavior Modeling in Recommender Systemscited by 26
  7. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  8. 2022CL4CTR: A Contrastive Learning Framework for CTR Predictioncited by 14
  9. 2024Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directionscited by 13
  10. 2022DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Predictioncited by 12
  11. 2022APG: Adaptive Parameter Generation Network for Click-Through Rate Predictioncited by 12
  12. 2023FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Predictioncited by 11
21 more
  1. 2023MAP: A Model-agnostic Pretraining Framework for Click-through Rate Predictioncited by 10
  2. 2023Unlocking the Transferability of Tokens in Deep Models for Tabular Datacited by 8
  3. 2021Click-through Rate Prediction with Auto-Quantized Contrastive Learningcited by 7
  4. 2023adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Adscited by 4
  5. 2026Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendationcited by 4
  6. 2023COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendationcited by 2
  7. 2025DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusioncited by 2
  8. 2026IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshucited by 2
  9. 2021FINT: Field-aware INTeraction Neural Network For CTR Predictioncited by 1
  10. 2023AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendationcited by 1
  11. 2023Reinforcement Learning from Statistical Feedback: the Journey from AB Testing to ANT Testingcited by 1
  12. 2023A Deep Behavior Path Matching Network for Click-Through Rate Predictioncited by 0
  13. 2023DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Predictioncited by 0
  14. 2023STUDY: Socially Aware Temporally Causal Decoder Recommender Systemscited by 0
  15. 2023EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through Rate Predictioncited by 0
  16. 2024An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM modelcited by 0
  17. 2025Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 0
  18. 2025Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Rankingcited by 0
  19. 2025Feature Staleness Aware Incremental Learning for CTR Predictioncited by 0
  20. 2025MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Pathscited by 0
  21. 2026Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Predictioncited by 0