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https://hdl.handle.net/1822/71227
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Campo DC | Valor | Idioma |
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dc.contributor.author | Pereira, Pedro José | por |
dc.contributor.author | Cortez, Paulo | por |
dc.contributor.author | Mendes, Rui | por |
dc.date.accessioned | 2021-04-02T18:56:02Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Pereira, P. J., Cortez, P., & Mendes, R. (2021). Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction. Expert Systems with Applications, 168, 114287. doi: https://doi.org/10.1016/j.eswa.2020.114287 | por |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://hdl.handle.net/1822/71227 | - |
dc.description.abstract | The worldwide adoption of mobile devices is raising the value of Mobile Performance Marketing, which is supported by Demand-Side Platforms (DSP) that match mobile users to advertisements. In these markets, monetary compensation only occurs when there is a user conversion. Thus, a key DSP issue is the design of a data-driven model to predict user conversion. To handle this nontrivial task, we propose a novel Multi-objective Optimization (MO) approach to evolve Decision Trees (DT) using a Grammatical Evolution (GE), under two main variants: a pure GE method (MGEDT) and a GE with Lamarckian Evolution (MGEDTL). Both variants evolve variable-length DTs and perform a simultaneous optimization of the predictive performance and model complexity. To handle big data, the GE methods include a training sampling and parallelism evaluation mechanism. The algorithms were applied to a recent database with around 6 million records from a real-world DSP. Using a realistic Rolling Window (RW) validation, the two GE variants were compared with a standard DT algorithm (CART), a Random Forest and a state-of-the-art Deep Learning (DL) model. Competitive results were obtained by the GE methods, which present affordable training times and very fast predictive response times. | por |
dc.description.sponsorship | This article 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 FCT – Fundação para a Ciência e Tecnologia, Portugal within the Project Scope: UID/CEC/00319/2019. We wish to thank the OLAmobile company for providing the data and domain feedback. We would also like to thank the anonymous reviewers for their helpful suggestions. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.relation | UID/CEC/00319/2019 | por |
dc.rights | restrictedAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Conversion Rate (CVR) prediction | por |
dc.subject | Decision Trees | por |
dc.subject | Explainable Artificial Intelligence (XAI) | por |
dc.subject | Grammatical Evolution | por |
dc.subject | Lamarckian Evolution | por |
dc.title | Multi-objective grammatical evolution of decision trees for mobile marketing user conversion prediction | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417420309891 | por |
oaire.citationIssue | 114287 | por |
oaire.citationVolume | 168 | por |
dc.identifier.doi | 10.1016/j.eswa.2020.114287 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Expert Systems with Applications | por |
oaire.version | VoR | por |
Aparece nas coleções: | DI/CCTC - Artigos (papers) |
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1-s2.0-S0957417420309891-main.pdf Acesso restrito! | 789,95 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons