Click prediction boosting via Bayesian hyperparameter optimization-based ensemble learning pipelines

The main goal of this article is to provide an overview of the use and characteristics of intelligent systems and neuroscience tools applicable in the field of contemporary advertising. The newly emerging field of computational advertising is undergoing dynamic development, and this concept is now placed in the context of advanced intelligent systems, artificial intelligence, and virtual reality. According to the specified parameters, a systematic literature search of scientific publications was carried out and subsequently evaluated. The research questions are focused on the identification of intelligent systems and current consumer neuroscience tools finding application in the current trend of computational advertising. It follows from the processed systematic literature review that there are currently a number of intelligent systems and also a number of tools in the field of consumer neuroscience that can find application within the broader concept of computational advertising. These more or less intelligent systems and neuroscientific tools are already affecting all phases of the advertising life cycle. At the same time, a number of ethical issues associated with the use of both these technologies and tools have been found, which still need to be explored. The article attempts to fill the gap in the lack of literature dealing with this issue. Last but not least, the article contains a critical view of these new technological possibilities and also describes a number of new ethical challenges arising in this area.

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Click prediction boosting via Bayesian hyperparameter optimization-based ensemble learning pipelines

Cited by 3 publications

References 42 publications

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Intelligent systems and consumer neuroscience in the age of computational advertising

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