Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87697
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Campo DC | Valor | Idioma |
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dc.contributor.author | Matos, Luis Miguel | por |
dc.contributor.author | Cortez, Paulo | por |
dc.contributor.author | Mendes, Rui | por |
dc.contributor.author | Moreau, Antoine | por |
dc.date.accessioned | 2023-12-28T14:19:15Z | - |
dc.date.available | 2023-12-28T14:19:15Z | - |
dc.date.issued | 2022-08 | - |
dc.identifier.issn | 0219-6220 | - |
dc.identifier.uri | https://hdl.handle.net/1822/87697 | - |
dc.description.abstract | In Mobile Performance Marketing (MPM), monetary compensation only occurs when an advertisement results in a conversion (e.g., sale of a product or service). In this work, we propose an intelligent decision support system (IDSS) to automatically select mobile marketing campaigns for users. The IDSS is based on a computationally efficient mobile user conversion prediction model that assumes a novel Percentage Categorical Pruning (PCP) categorical preprocessing and an online deep multilayer perceptron (MLP) reuse model (MLPr). Using private (nonpublicly available) business MPM data provided by a marketing company, the MLPr model outperformed an offline multilayer perceptron and a logistic regression, obtaining a high quality class discrimination when applied to sampled (85% to 92%) and complete (90% to 94%) data. In addition, the MLPr compared favorably with other machine learning (ML) models (e.g., Random Forest, XGBoost), as well as with other deep neural networks (e.g., diamond shaped). Moreover, we designed two strategies (A - best campaign selection; and B - random selection among the top candidate campaigns) to build the IDSS, in which the predictive deep learning model is used to perform a real-time selection of advertisement campaigns for mobile users. Using recently collected big data (with millions of redirect events) from a worldwide MPM company, we performed a realistic IDSS evaluation that considered three criteria: response time, potential profit and advertiser diversity. Overall, competitive results were achieved by the IDSS B strategy when compared with the current marketing company ad assignment method. | por |
dc.description.sponsorship | This paper is a result of the project NORTE-01-0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-010145-FEDER-007043 and FCT Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. Finally, we wish to thank the anonymous reviewers for their helpful suggestions. | por |
dc.language.iso | eng | por |
dc.publisher | World Scientific Publishing | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F00319%2F2013/PT | por |
dc.rights | openAccess | por |
dc.subject | Big data | por |
dc.subject | Categorical transformation | por |
dc.subject | Classification | por |
dc.subject | Conversion Rate (CVR) | por |
dc.subject | Deep multilayer perceptron | por |
dc.subject | Intelligent decision support system (IDSS) | por |
dc.title | A deep learning-based decision support system for mobile performance marketing | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.worldscientific.com/doi/abs/10.1142/S021962202250047X | por |
oaire.citationStartPage | 679 | por |
oaire.citationEndPage | 703 | por |
oaire.citationIssue | 2 | por |
oaire.citationVolume | 22 | por |
dc.date.updated | 2023-12-27T18:07:15Z | - |
dc.identifier.eissn | 1793-6845 | - |
dc.identifier.doi | 10.1142/S021962202250047X | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 12631 | - |
sdum.journal | International Journal of Information Technology & Decision Making | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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A deep learning-based decision(...).pdf | 226,51 kB | Adobe PDF | Ver/Abrir |