ArticleJournal of big data2023
CRNet: a multimodal deep convolutional neural network for customer revisit prediction.
Article in Journal of big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Meta-LLSTM: meta-learning enhanced learnable LSTM for retail sales forecasting.Scientific reports · 2026Article
- Enhancing InceptionResNet to Diagnose COVID-19 from Medical Images.Current medicinal chemistry · 2026Article
- Detection of Anomalies in Data Streams Using the LSTM-CNN Model.Sensors (Basel, Switzerland) · 2025Article
- Development and Validation of Data-Level Innovation Data-Balancing Machine Learning Models for Predicting Optimal Implantable Collamer Lens Size and Postoperative Vault.Ophthalmology and therapy · 2024Article
- Efficient security level in wireless sensor networks (WSNs) using four-factors authentication over the Internet of Things (IoT).PeerJ. Computer science · 2024Article
- Object detection in optical imaging of the Internet of Things based on deep learning.PeerJ. Computer science · 2023Article
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1 author.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.