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TitleUsing deep learning for ordinal classification of mobile marketing user conversion
Author(s)Matos, Luís Miguel
Cortez, Paulo
Mendes, Rui
Moreau, Antoine
KeywordsMobile Performance Marketing
Multilayer Perceptron
Ordinal Classification
Issue date2019
PublisherSpringer Nature
JournalLecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
CitationIn H. Yin et al., Intelligent Data Engineering and Automated Learning (IDEAL 2019), 20th International Conference, Lecture Notes in Computer Science 11871, Part I, pp. 60-67, Manchester, UK, November 2019, Springer, ISBN 978-3-030-33607-3.
Abstract(s)In this paper, we explore Deep Multilayer Perceptrons (MLP) to perform an ordinal classification of mobile marketing conversion rate (CVR), allowing to measure the value of product sales when an user clicks an ad. As a case study, we consider big data provided by a global mobile marketing company. Several experiments were held, considering a rolling window validation, different datasets, learning methods and performance measures. Overall, competitive results were achieved by an online deep learning model, which is capable of producing real-time predictions.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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