CRNet: a multimodal deep convolutional neural network for customer revisit prediction

Eunil Park

doi:10.1186/s40537-022-00674-4

CRNet: a multimodal deep convolutional neural network for customer revisit prediction

J Big Data. 2023;10(1):1. doi: 10.1186/s40537-022-00674-4. Epub 2023 Jan 3.

Author

Eunil Park^{1

2

3}

Affiliations

¹ Department of Interaction Science, Sungkyunkwan University, Seoul, Republic of Korea.
² Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea.
³ Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, Republic of Korea.

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.

Keywords: CRNet; Customer repurchase; Customer revisit; MFDRD.