A Review of Click-Through Rate Prediction Using Deep Learning

Abstract

Online advertising is vital for reaching target audiences and promoting products. In 2020, US online advertising revenue increased by 12.2% to $139.8 billion. The industry is projected to reach $487.32 billion by 2030. Artificial intelligence has improved click-through rates (CTR), enabling personalized advertising content by analyzing user behavior and providing real-time predictions. This review examines the latest CTR prediction solutions, particularly those based on deep learning, over the past three years. This timeframe was chosen because CTR prediction has rapidly advanced in recent years, particularly with transformer architectures, multimodal fusion techniques, and industrial applications. By focusing on the last three years, the review highlights the most relevant developments not covered in earlier surveys. This review classifies CTR prediction methods into two main categories: CTR prediction techniques employing text and CTR prediction approaches utilizing multivariate data. The methods that use multivariate data to predict CTR are further categorized into four classes: graph-based methods, feature-interaction-based techniques, customer-behavior approaches, and cross-domain methods. The review also outlines current challenges and future research opportunities. The review highlights that graph-based and multimodal methods currently dominate state-of-the-art CTR prediction, while feature-interaction and cross-domain approaches provide complementary strengths. These key takeaways frame open challenges and emerging research directions.