E-Commerce User Purchase Prediction Analysis Based on Data Mining
Abstract
In the Internet era, various e-commerce platforms are gradually rising, people's consumption has gradually changed from offline to online. How to predict users' purchasing behavior based on big data has become an important challenge for e-commerce platform. Based on the knowledge of e-commerce, this paper constructs a funnel model to analyze the user conversion from each link, so as to locate the link with low conversion rate. The data mining method is used to process the user history data of an international e-commerce company. And through the importance of features,we can know from which aspects to promote the transformation of users, so as to take targeted measures to users.
Keywords
Full Text:
PDFReferences
Zhao TY, Liu SC, Xu J, et al. Comparative analysis of seven machine learning algorithms and five empirical models to estimate soil thermal conductivity[J]. Agricultural and Forest Meteorology, 2022, 323.
Yan HW, Huo XR, Liu JJ. Research And Development of Suitable Aging Platform Based on Middle-Aged And Elderly Online Shopping Market Research[J]. International Journal of Education and Teaching Research, 2022, 3(2).
Hou J, Li QM, Liu YZ, Zhang SN. An Enhanced Cascading Model for E-Commerce Consumer Credit Default Prediction[J]. Journal of Organizational and End User Computing (JOEUC), 2021, 33(6).
Chang H, Yang SQ. Research on Commodity Mixed Recommendation Algorithm[J]. International Journal of Advanced Network, Monitoring and Controls, 2020, 5(3).
DOI: http://dx.doi.org/10.18686/fm.v7i3.4531
Refbacks
- There are currently no refbacks.
