A dual bidding model for performance advertising based on deep learning

Over the past few years, the widespread adoption of the Internet has spurred swift growth in the field of performance advertising. However, Performance advertising can be conceptualized as a game scenario, wherein media platforms aim to maximize the revenue generated from traffic, while advertisement clients seek to ensure that the costs associated with advertising delivery and subsequent deep performance align with their expectations. The paper begins by examining the latest progress in the evolution of performance advertising through bidding models. It then proceeds to summarize the strengths and weaknesses of these bid models. To address these shortcomings, the paper proposed a dual bidding model of performance advertising based on deep learning. The results show that the ROI is improved by 7.55%, the retained placement is improved by 5.83%, and the paid placement is improved by 9.84%.