# Click-Through Rate Prediction in Online Advertising: A Literature Review

arXiv: https://arxiv.org/abs/2202.10462 · HTML: https://mlsanity.com/paper/2202.10462/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 169 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 30 of 57)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [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)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [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)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [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)
- [Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems](https://mlsanity.com/paper/1909.02107/) (2019)
- [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://mlsanity.com/paper/1601.02376/) (2016)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (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)
- [Learnable Embedding Sizes for Recommender Systems](https://mlsanity.com/paper/2101.07577/) (2021)
- [AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications](https://mlsanity.com/paper/1904.12857/) (2019)
- [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction](https://mlsanity.com/paper/2007.06434/) (2020)
- [An Embedding Learning Framework for Numerical Features in CTR Prediction](https://mlsanity.com/paper/2012.08986/) (2020)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
- [Deep CTR Prediction in Display Advertising](https://mlsanity.com/paper/1609.06018/) (2016)
- [FM2: Field-matrixed Factorization Machines for Recommender Systems](https://mlsanity.com/paper/2102.12994/) (2021)
- [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1904.04447/) (2019)
- [Deep reinforcement learning for search, recommendation, and online advertising: a survey](https://mlsanity.com/paper/1812.07127/) (2018)
- [Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks](https://mlsanity.com/paper/1404.5772/) (2014)

## Built on by (most cited first, 30 of 31)

- [Ads Recommendation in a Collapsed and Entangled World](https://mlsanity.com/paper/2403.00793/) (2024)
- [DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction](https://mlsanity.com/paper/2203.11014/) (2022)
- [DeepGen: Diverse Search Ad Generation and Real-Time Customization](https://mlsanity.com/paper/2208.03438/) (2022)
- [Causality-based CTR Prediction using Graph Neural Networks](https://mlsanity.com/paper/2301.12762/) (2023)
- [UniPredict: Large Language Models are Universal Tabular Classifiers](https://mlsanity.com/paper/2310.03266/) (2023)
- [Be.FM: Open Foundation Models for Human Behavior](https://mlsanity.com/paper/2505.23058/) (2025)
- [TCE: A Test-Based Approach to Measuring Calibration Error](https://mlsanity.com/paper/2306.14343/) (2023)
- [Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching](https://mlsanity.com/paper/2210.15459/) (2022)
- [AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising](https://mlsanity.com/paper/2408.05906/) (2024)
- [General Information Metrics for Improving AI Model Training Efficiency](https://mlsanity.com/paper/2501.02004/) (2025)
- [Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction](https://mlsanity.com/paper/2505.14057/) (2025)
- [Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation](https://mlsanity.com/paper/2501.01367/) (2025)
- [Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agenda](https://mlsanity.com/paper/2302.12372/) (2023)
- [Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions](https://mlsanity.com/paper/2306.01799/) (2023)
- [Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings](https://mlsanity.com/paper/2306.04064/) (2023)
- [Impression-Aware Recommender Systems](https://mlsanity.com/paper/2308.07857/) (2023)
- [Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction](https://mlsanity.com/paper/2408.08713/) (2024)
- [Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction](https://mlsanity.com/paper/2409.14945/) (2024)
- [Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao](https://mlsanity.com/paper/2411.13057/) (2024)
- [Dataset-Agnostic Recommender Systems](https://mlsanity.com/paper/2501.07294/) (2025)
- [BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks](https://mlsanity.com/paper/2606.24162/) (2026)
- [Beyond Rankings: Exploring the Impact of SERP Features on Organic Click-through Rates](https://mlsanity.com/paper/2306.01785/) (2023)
- [EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through Rate Prediction](https://mlsanity.com/paper/2310.04482/) (2023)
- [GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model](https://mlsanity.com/paper/2402.13667/) (2024)
- [NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction](https://mlsanity.com/paper/2409.08703/) (2024)
- [CTR Prediction on Alibaba's Taobao Advertising Dataset Using Traditional and Deep Learning Models](https://mlsanity.com/paper/2511.21963/) (2025)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives](https://mlsanity.com/paper/2512.01179/) (2025)
- [Competition-Aware CPC Forecasting with Near-Market Coverage](https://mlsanity.com/paper/2603.13059/) (2026)
- [Generative Long-term User Interest Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/2605.15905/) (2026)

## Abstract (arXiv)

Predicting the probability that a user will click on a specific advertisement has been a prevalent issue in online advertising, attracting much research attention in the past decades. As a hot research frontier driven by industrial needs, recent years have witnessed more and more novel learning models employed to improve advertising CTR prediction. Although extant research provides necessary details on algorithmic design for addressing a variety of specific problems in advertising CTR prediction, the methodological evolution and connections between modeling frameworks are precluded. However, to the best of our knowledge, there are few comprehensive surveys on this topic. We make a systematic literature review on state-of-the-art and latest CTR prediction research, with a special focus on modeling frameworks. Specifically, we give a classification of state-of-the-art CTR prediction models in the extant literature, within which basic modeling frameworks and their extensions, advantages and disadvantages, and performance assessment for CTR prediction are presented. Moreover, we summarize CTR prediction models with respect to the complexity and the order of feature interactions, and performance comparisons on various datasets. Furthermore, we identify current research trends, main challenges and potential future directions worthy of further explorations. This review is expected to provide fundamental knowledge and efficient entry points for IS and marketing scholars who want to engage in this area.