Publication
A deep learning-based decision support system for mobile performance marketing
| Summary: | 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. |
|---|---|
| Main Authors: | Matos, Luis Miguel |
| Other Authors: | Cortez, Paulo; Mendes, Rui; Moreau, Antoine |
| Subject: | Big data Categorical transformation Classification Conversion Rate (CVR) Deep multilayer perceptron Intelligent decision support system (IDSS) |
| Year: | 2022 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Universidade do Minho |
| Language: | English |
| Origin: | RepositóriUM - Universidade do Minho |
| _version_ | 1867439309895761920 |
|---|---|
| author | Matos, Luis Miguel |
| author2 | Cortez, Paulo Mendes, Rui Moreau, Antoine |
| author2_role | author author author |
| author_facet | Matos, Luis Miguel Cortez, Paulo Mendes, Rui Moreau, Antoine |
| author_role | author |
| contributor_name_str_mv | RepositóriUM - Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Matos, Luis Miguel\"},{\"Person.name\":\"Cortez, Paulo\"},{\"Person.name\":\"Mendes, Rui\"},{\"Person.name\":\"Moreau, Antoine\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RepositóriUM - Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Matos, Luis Miguel Cortez, Paulo Mendes, Rui Moreau, Antoine |
| datacite.date.Accepted.fl_str_mv | 2022-08-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-12-28T14:19:15Z |
| datacite.date.embargoed.fl_str_mv | 2023-12-28T14:19:15Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Big data Categorical transformation Classification Conversion Rate (CVR) Deep multilayer perceptron Intelligent decision support system (IDSS) |
| datacite.titles.title.fl_str_mv | A deep learning-based decision support system for mobile performance marketing |
| dc.contributor.none.fl_str_mv | RepositóriUM - Universidade do Minho |
| dc.creator.none.fl_str_mv | Matos, Luis Miguel Cortez, Paulo Mendes, Rui Moreau, Antoine |
| dc.date.Accepted.fl_str_mv | 2022-08-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-12-28T14:19:15Z |
| dc.date.embargoed.fl_str_mv | 2023-12-28T14:19:15Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/87697 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | World Scientific Publishing |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Big data Categorical transformation Classification Conversion Rate (CVR) Deep multilayer perceptron Intelligent decision support system (IDSS) |
| dc.title.fl_str_mv | A deep learning-based decision support system for mobile performance marketing |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/4d392694-b794-431b-946c-b35c0f095078/download |
| id | rum_fbb3d0697de52e3df190aa9a3fc8cd4a |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/87697 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/87697 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Matos, Luis Miguel Cortez, Paulo Mendes, Rui Moreau, Antoine |
| publishDate | 2022 |
| publisher.none.fl_str_mv | World Scientific Publishing |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engWorld Scientific PublishingporIn 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.application/pdfporA deep learning-based decision support system for mobile performance marketingMatos, Luis MiguelCortez, PauloMendes, RuiMoreau, AntoineHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf0219-6220EISSNIsPartOf1793-6845DOIIsPartOf10.1142/S021962202250047X2023-12-28T14:19:15Z2022-082023-12-27T18:07:15Z2022-08-01T00:00:00ZHandlehttps://hdl.handle.net/1822/87697http://purl.org/coar/access_right/c_abf2open accessBig dataCategorical transformationClassificationConversion Rate (CVR)Deep multilayer perceptronIntelligent decision support system (IDSS)231947 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/4d392694-b794-431b-946c-b35c0f095078/download |
| spellingShingle | A deep learning-based decision support system for mobile performance marketing Matos, Luis Miguel Big data Categorical transformation Classification Conversion Rate (CVR) Deep multilayer perceptron Intelligent decision support system (IDSS) |
| status | SINGLETON |
| subject.fl_str_mv | Big data Categorical transformation Classification Conversion Rate (CVR) Deep multilayer perceptron Intelligent decision support system (IDSS) |
| title | A deep learning-based decision support system for mobile performance marketing |
| title_full | A deep learning-based decision support system for mobile performance marketing |
| title_fullStr | A deep learning-based decision support system for mobile performance marketing |
| title_full_unstemmed | A deep learning-based decision support system for mobile performance marketing |
| title_short | A deep learning-based decision support system for mobile performance marketing |
| title_sort | A deep learning-based decision support system for mobile performance marketing |
| topic | Big data Categorical transformation Classification Conversion Rate (CVR) Deep multilayer perceptron Intelligent decision support system (IDSS) |
| topic_facet | Big data Categorical transformation Classification Conversion Rate (CVR) Deep multilayer perceptron Intelligent decision support system (IDSS) |
| url | https://hdl.handle.net/1822/87697 |
| visible | 1 |