Safara Fatemeh
Department of Computer Engineering, Islamic Azad University, Islamshahr Branch, Islamshahr, Iran.
Comput Econ. 2022;59(4):1525-1538. doi: 10.1007/s10614-020-10069-3. Epub 2020 Nov 5.
The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.
近年来，知识经济受到了越来越多的关注，尤其是在所有交易和消费者意见都有记录的在线购物应用中。机器学习方法可用于从日志中提取隐含知识。行业和企业利用这些知识来更好地了解消费者行为以及相应的机遇和威胁。冠状病毒（COVID-19）大流行的爆发对我们日常生活的各个方面都产生了重大影响，尤其是对我们的购物行为。预测电子消费者行为对政府、供应链和零售行业的管理者可能会有很大帮助。虽然在冠状病毒大流行之前我们就已经体验过在线购物，但在疫情期间，在线购物的数量急剧增加。由于COVID-19的高速传播，我们必须遵守个人和社会健康问题，如保持社交距离和居家。这些问题对在线购物中的消费者行为有直接影响。在本文中，提出了一种使用机器学习方法来预测消费者行为的模型。在从一个在线购物网站收集的数据集上，研究了五个单独的分类器及其通过装袋法和提升法形成的集成分类器。结果表明，使用带有装袋法的决策树集成构建的模型对消费者行为的预测效果最佳，准确率达到了95.3%。此外，还进行了相关性分析，以确定在冠状病毒大流行期间影响在线购买量的最重要特征。
