Click Through Rate

Click-through rate (CTR) prediction aims to accurately estimate the probability of a user clicking on a given item, crucial for optimizing online advertising and recommender systems. Current research focuses on improving CTR prediction accuracy through advanced model architectures like Deep Interest Networks (DIN), transformers, and large language models (LLMs), often incorporating techniques such as feature selection, knowledge distillation, and bias mitigation to address challenges like data sparsity and position bias. These advancements significantly impact online businesses by enhancing ad targeting, personalization, and ultimately, revenue generation. Furthermore, ongoing work explores efficient training methods and addresses the complexities of handling massive datasets and concept drift in real-world applications.