  This paper proposes new methods to enhance click-through rate (CTR)
prediction models using the Deep Interest Network (DIN) model, specifically
applied to the advertising system of Alibaba's Taobao platform. Unlike
traditional deep learning approaches, this research focuses on localized user
behavior activation for tailored ad targeting by leveraging extensive user
behavior data. Compared to traditional models, this method demonstrates
superior ability to handle diverse and dynamic user data, thereby improving the
efficiency of ad systems and increasing revenue.
