Click Through Rate Prediction

Click-through rate (CTR) prediction aims to accurately estimate the probability of a user clicking on a recommended item, crucial for optimizing online advertising and recommender systems. Current research emphasizes efficient modeling of high-order feature interactions, often employing deep learning architectures like factorization machines, transformers, and neural networks with specialized modules for handling diverse data distributions and user behaviors (e.g., incorporating contextual information, lifelong user history, and multimodal data). These advancements improve prediction accuracy and efficiency, impacting the effectiveness of personalized recommendations and online advertising strategies. Furthermore, research explores techniques to address challenges like data sparsity, cold-start problems, and computational limitations in large-scale deployments.