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

Abstract

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.

85 pages, 12 figures, 9 tables

References in corpus (21)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
• Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
• Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
• $F{M}^2$: Field-matrixed Factorization Machines for Recommender Systems
• Real-Time Bidding Benchmarking with iPinYou Dataset
• Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
• Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
• Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
• Network On Network for Tabular Data Classification in Real-world Applications
• Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models
• Deep Character-Level Click-Through Rate Prediction for Sponsored Search
• TFNet: Multi-Semantic Feature Interaction for CTR Prediction
• Deep Position-wise Interaction Network for CTR Prediction
• Differentiable Neural Input Search for Recommender Systems
• Learnable Embedding Sizes for Recommender Systems
• Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study
• A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction
• PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
• Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate
• Feature Interaction based Neural Network for Click-Through Rate Prediction

Cited by in corpus (3)

• Causality-based CTR Prediction using Graph Neural Networks
• Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agenda
• Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching