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Deep learning based customer preferences analysis in industry 4.0 environment

Sun, Qindong; Feng, Xingyu; Zhao, Shanshan; Cao, Han; Li, Shancang; Yao, Yufeng

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Authors

Qindong Sun

Xingyu Feng

Shanshan Zhao

Han Cao

Shancang Li Shancang.Li@uwe.ac.uk
Senior Lecturer in Computer Forensics and Security

Yufeng Yao Yufeng.Yao@uwe.ac.uk
Professor in Aerospace Engineering



Abstract

Customer preferences analysis and modelling using deep learning in edge computing environment are critical to enhance customer relationship management that focus on a dynamically changing market place. Existing forecasting methods work well with often seen and linear demand patterns but become less accurate with intermittent demands in the catering industry. In this paper, we introduce a throughput deep learning model for both short-term and long-term demands forecasting aimed at allowing catering businesses to be highly efficient and avoid wastage. Moreover, detailed data collected from a business online booking system in the past three years have been used to train and verify the proposed model. Meanwhile, we carefully analyzed the seasonal conditions as well as past local or national events (event analysis) that could have had critical impact on the sales. The results are compared with the best performing forecast methods Xgboost and autoregressive moving average model (ARMA), and they suggest that the proposed method significantly improves demand forecasting accuracy (up to 80%) for dishes demand along with reduction in associated costs and labor allocation.

Citation

Sun, Q., Feng, X., Zhao, S., Cao, H., Li, S., & Yao, Y. (2021). Deep learning based customer preferences analysis in industry 4.0 environment. Mobile Networks and Applications, 26, 2329–2340. https://doi.org/10.1007/s11036-021-01830-5

Journal Article Type Article
Acceptance Date Jan 15, 2020
Online Publication Date Jan 14, 2022
Publication Date 2021-12
Deposit Date May 23, 2020
Publicly Available Date Mar 28, 2024
Journal Mobile Networks and Applications
Print ISSN 1383-469X
Electronic ISSN 1572-8153
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 26
Pages 2329–2340
DOI https://doi.org/10.1007/s11036-021-01830-5
Public URL https://uwe-repository.worktribe.com/output/5261083
Publisher URL https://www.springer.com/journal/11036

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