Optimized Deep Transfer Learning Framework for Accurate Liver Tumor Classification

Abstract

Within the competitive and complicated environment of the retail media networks (RMN), advertisers are experiencing pressure to be more precise in ad spending. Conventional indicators such as Return on Ad Spend (ROAS) are normally inflated to reflect the performance of a campaign by ignoring organic conversions, which encourages practitioners to use incremental ROAS (iROAS) as a more precise metric. This paper will discuss the combination of iROAS and Multi-Armed Bandit (MAB) algorithms to form a dynamic bidding strategy that will evolve in real time. Using causal inference to estimate iROAS and MAB models to trade off exploration and exploitation, advertisers can efficiently optimize budgets and make ongoing progress in media performance. The paper provides a theoretical background, a scalable architecture of implementation, and addresses the problem of delayed incentives, cold-start environments, preservation of privacy, and exploration of many-objective optimization. Overall, it provides a causally responsive method of automated bidding, which causes responsible and value-based advertising in RMNs.