Research on In-depth Intelligent Optimization of Sponsored Search Advertising Content on E-commerce Platforms

Abstract

With the rapid development of e-commerce market, online shopping has been integrated into many people’s daily life. In order to optimize the advertising content sponsored by e-commerce platform, we build the selling point keyword prediction model of advertising products based on multi-task deep learning. The experimental results show that compared with the basic model, the click-through rate of advertising products is improved by 0.48%. After introducing additional feature information, the AUC result of the model increased by 0.79%, effectively optimizing the advertising content of sponsored search, enhancing the user’s personalized buying experience, and providing new research ideas for the in-depth intelligent optimization of sponsored search by e-commerce platform.