Research on E-commerce Sales Forecasting Method Based on Data Mining

：曹菁菁（武汉理工大学）
：2022-11-16 14:30
：腾讯会议ID：579-922-4321（无密码）

报告人：曹菁菁（武汉理工大学）

时  间：11月16日下午14:30-16:00

地  点：腾讯会议ID：579-922-4321（无密码）

内容摘要:

Data Mining is one of the important research directions in the field of e-commerce, especially in the prediction of e-commerce sales. In the actual scenario of e-commerce platform, users, goods and merchants and the data generated among them contain rich information and heterogeneous features, how to consider the impact of various characteristics on sales forecast, and tap the hidden needs of users, are the urgent problems to be solved. This report will introduce four data mining-based e-commerce sales forecasting methods from the multi-dimensional perspectives of customer portrait, customer segmentation, and sentiment analysis for the two tasks of predicting the purchase and repurchase behavior of e-commerce users. These methods are designed to efficiently extract user or product features to improve the accuracy and interpretability of predictive models.

个人简介：

曹菁菁，武汉理工大学副教授，CCF普适计算、协同计算专委会委员，香港城市大学计算机科学系博士，主要研究方向是机器学习及其应用，包括人体行为识别、物流需求预测，工业缺陷检测等领域的应用研究。主持完成一项国家自然科学基金青年项目、主持或参与国家自然科学青年、面上基金、科技部重点研发等多个纵向和企业委托项目。在Neural Computing and Applications、Information Fusion、Neurocomputing、Pattern Recognition、Sensors、Information Sciences等重要期刊和会议发表论文30余篇。发明专利授权3项，软著6项。担任多个国际期刊和会议的审稿人和PC委员，被国际期刊Neurocomputing评为杰出审稿人。

 

联系人：周达