Click-through rate prediction in online advertising: A literature review

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