Application of Bayesian Optimization in Periodic Delivery

In modern e-commerce advertising, how to efficiently allocate advertising budgets to maximize the effect (i.e., impressions/cost) is an important challenge. Traditionally, advertising budget allocation relies on experience and manual judgment. Although it is effective in some cases, it often lacks systematicity and precision and is difficult to adapt to the complex and changing market environment. More and more merchants are eager to know which factors to consider and which aspects can improve the conversion rate of advertisements at a lower cost, thereby increasing sales revenue and making better profits. This paper proposes an automated advertising delivery strategy based on Bayesian optimization and Gaussian process regression (GPR) model. By combining the periodic characteristics of time with cost, the model can dynamically adjust the delivery strategy in different time periods to maximize the impression volume per unit cost. Experimental results show that compared with the traditional empirical delivery method, this automated method significantly improves the efficiency of advertising delivery. In practical applications, this method can not only predict the optimal delivery strategy more accurately, but also improve its responsiveness to market changes through continuous learning and optimization. This study provides an effective intelligent solution for e-commerce advertising delivery, which has high application value and promotion prospects.