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CRNet: a multimodal deep convolutional neural network for customer revisit prediction
Since mobile food delivery services have become one of the essential issues for the restaurant industry, predicting customer revisits is highlighted as one of the significant academic and research topics. Considering that the use of multimodal datasets has gained notable attention from several schol...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808691/ https://www.ncbi.nlm.nih.gov/pubmed/36618886 http://dx.doi.org/10.1186/s40537-022-00674-4 |
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author | Park, Eunil |
author_facet | Park, Eunil |
author_sort | Park, Eunil |
collection | PubMed |
description | Since mobile food delivery services have become one of the essential issues for the restaurant industry, predicting customer revisits is highlighted as one of the significant academic and research topics. Considering that the use of multimodal datasets has gained notable attention from several scholars to address multiple industrial issues in our society, we introduce CRNet, a multimodal deep convolutional neural network for predicting customer revisits. We evaluated our approach using two datasets [a customer repurchase dataset (CRD) and mobile food delivery revisit dataset (MFDRD)] and two state-of-the-art multimodal deep learning models. The results showed that CRNet obtained accuracies and Fi-Scores of 0.9575 (CRD) and 0.9436 (MFDRD) and 0.9730 (CRD) and 0.9509 (MFDRD), respectively, thus achieving higher performance levels than current state-of-the-art multimodal frameworks (accuracy: 0.7417–0.9012; F1-Score: 0.7461–0.9378). Future research should aim to address other resources that can enhance the proposed framework (e.g., metadata information). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40537-022-00674-4. |
format | Online Article Text |
id | pubmed-9808691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-98086912023-01-04 CRNet: a multimodal deep convolutional neural network for customer revisit prediction Park, Eunil J Big Data Research Since mobile food delivery services have become one of the essential issues for the restaurant industry, predicting customer revisits is highlighted as one of the significant academic and research topics. Considering that the use of multimodal datasets has gained notable attention from several scholars to address multiple industrial issues in our society, we introduce CRNet, a multimodal deep convolutional neural network for predicting customer revisits. We evaluated our approach using two datasets [a customer repurchase dataset (CRD) and mobile food delivery revisit dataset (MFDRD)] and two state-of-the-art multimodal deep learning models. The results showed that CRNet obtained accuracies and Fi-Scores of 0.9575 (CRD) and 0.9436 (MFDRD) and 0.9730 (CRD) and 0.9509 (MFDRD), respectively, thus achieving higher performance levels than current state-of-the-art multimodal frameworks (accuracy: 0.7417–0.9012; F1-Score: 0.7461–0.9378). Future research should aim to address other resources that can enhance the proposed framework (e.g., metadata information). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40537-022-00674-4. Springer International Publishing 2023-01-03 2023 /pmc/articles/PMC9808691/ /pubmed/36618886 http://dx.doi.org/10.1186/s40537-022-00674-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Park, Eunil CRNet: a multimodal deep convolutional neural network for customer revisit prediction |
title | CRNet: a multimodal deep convolutional neural network for customer revisit prediction |
title_full | CRNet: a multimodal deep convolutional neural network for customer revisit prediction |
title_fullStr | CRNet: a multimodal deep convolutional neural network for customer revisit prediction |
title_full_unstemmed | CRNet: a multimodal deep convolutional neural network for customer revisit prediction |
title_short | CRNet: a multimodal deep convolutional neural network for customer revisit prediction |
title_sort | crnet: a multimodal deep convolutional neural network for customer revisit prediction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808691/ https://www.ncbi.nlm.nih.gov/pubmed/36618886 http://dx.doi.org/10.1186/s40537-022-00674-4 |
work_keys_str_mv | AT parkeunil crnetamultimodaldeepconvolutionalneuralnetworkforcustomerrevisitprediction |