The Use of Recurrent Nets for the Prediction of e-Commerce Sales

被引：0
作者：
Aldhahri, Eman [1 ]
机构：
[1] Univ Jeddah, Dept Comp Sci & Artificial Intelligence, Coll Comp Sci & Engn, Jeddah, Saudi Arabia
来源：
ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH | 2023年 / 13卷 / 03期
关键词：
e-commerce; sales; deep learning; prediction; RNNs;
D O I：
10.48084/etasr.5964
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance.
引用
收藏
页码：10931 / 10935
页数：5
相关论文
共 31 条
[1]  
Ali W, 2023, ENG TECHNOL APPL SCI, V13, P10051
[2]   Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology [J].
Bandara, Kasun ;
Shi, Peibei ;
Bergmeir, Christoph ;
Hewamalage, Hansika ;
Quoc Tran ;
Seaman, Brian .
NEURAL INFORMATION PROCESSING (ICONIP 2019), PT III, 2019, 11955 :462-474
[3]   Data-driven agent-based exploration of customer behavior [J].
Bell, David ;
Mgbemena, Chidozie .
SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, 2018, 94 (03) :195-212
[4]   Learning multiple layers of knowledge representation for aspect based sentiment analysis [J].
Duc-Hong Pham ;
Anh-Cuong Le .
DATA & KNOWLEDGE ENGINEERING, 2018, 114 :26-39
[5]  
Fangyu WU, 2020, 2020 International Conference on Machine Learning and Cybernetics (ICMLC), P229, DOI 10.1109/ICMLC51923.2020.9469551
[6]  
Geron Aurelien, 2017, Hands-on machine learning with Scikit-Learn and TensorFlow: Concepts, tools, and techniques to build intelligent systems
[7]   Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks [J].
Hanson, Jack ;
Peliwal, Kuldip ;
Litfin, Thomas ;
Yang, Yuedong ;
Zhou, Yaoqi .
BIOINFORMATICS, 2018, 34 (23) :4039-4045
[8]   LSTM with particle Swam optimization for sales forecasting [J].
He, Qi-Qiao ;
Wu, Cuiyu ;
Si, Yain-Whar .
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2022, 51
[9]  
Islam K. F., 2022, INT C STEM 4 IND REV, P474, DOI [10.53808/KUS.2022.ICSTEM4IR.0082-se, DOI 10.53808/KUS.2022.ICSTEM4IR.0082-SE]
[10]   Recommendations-based on semantic analysis of social networks in learning environments [J].
Khaled, Abdelaziz ;
Ouchani, Samir ;
Chohra, Chemseddine .
COMPUTERS IN HUMAN BEHAVIOR, 2019, 101 :435-449
1234