A methodology based on deep learning for advert value calculation in CPM, CPC and CPA networks
MetadataShow full item record
In this research, we propose a methodology for advert value calculation in CPM, CPC and CPA networks. Accurately estimating this value increases the three previous networks’ incomes by selecting the most profitable advert. By increasing income, publishers are better paid and improved services are afforded to advertisers. To develop this methodology, we propose a system based on traditional Machine Learning methods and Deep Learning methods. The system has two inputs and one output. The inputs are the user visit and the data about the advertiser. The output is the advert value expressed in dollars. Deep Learning predicts model behavior more precisely for many supervised problems. The three experiments carried out allow us to conclude that DL is a supervised method that is very efficient in the classification of spam adverts and in the estimation of the CTR. In the prediction of online sales, DLNN have shown, on average, worse performance than cubist and random forest methods, although better performance than model tree, model rules and linear regression methods. © 2016, Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.com.
Showing items related by title, author, creator and subject.
Stochastic parallel extreme artificial hydrocarbon networks : an implementation for fast and robust supervised machine learning in high-dimensional data Ponce, Hiram; González Mora, José Guillermo (Elsevier Ltd., 2020-03)Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other ...
A novel artificial hydrocarbon networks based value function approximation in hierarchical reinforcement learning Ponce, Hiram (Springer Verlag, 2017)Reinforcement learning aims to solve the problem of learning optimal or near-optimal decision-making policies for a given domain problem. However, it is known that increasing the dimensionality of the input space (i.e. ...
A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks Ponce, Hiram; Martinez-Villaseñor, Lourdes; Miralles, Luis (MDPI AG, 2016)Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of ...