Using deep learning for ordinal classification of mobile marketing user conversion

doi:10.1007/978-3-030-33607-3_7

Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/62742

Título:	Using deep learning for ordinal classification of mobile marketing user conversion
Autor(es):	Matos, Luís Miguel Cortez, Paulo Mendes, Rui Moreau, Antoine
Palavras-chave:	Mobile Performance Marketing Multilayer Perceptron Ordinal Classification
Data:	2019
Editora:	Springer Nature
Revista:	Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Citação:	In 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.
Resumo(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.
Tipo:	Artigo em ata de conferência
URI:	https://hdl.handle.net/1822/62742
ISBN:	978-3-030-33607-3
DOI:	10.1007/978-3-030-33607-3_7
ISSN:	0302-9743
Versão da editora:	https://link.springer.com/chapter/10.1007/978-3-030-33607-3_7
Arbitragem científica:	yes
Acesso:	Acesso aberto
Aparece nas coleções:	CAlg - Artigos em livros de atas/Papers in proceedings