Open AccessComprehensive Analysis and Mining Big Data on Smart E-commerce User Behavior
Author(s) -
Yinggui Wang,
Ben Wang,
Yuxin Huang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012016
Subject(s) - big data , e commerce , computer science , data science , consumer behaviour , pareto principle , world wide web , data mining , engineering , business , advertising , operations management
With the gradual development of big data technology and the rapid growth of e-commerce industry, big data comprehensive analysis technology is particularly important. Therefore, the user behavior and historical shopping data have been focused in recent progress of e-commerce industry. Based on e-commerce user data sets, this paper analyzes the user behavior based on Pareto principle (80-20 rule) and RFM data models. By statistically analyzing the behavior characteristics of users, user groups, user preferences, and purchase behavior characteristics, several models and research algorithms have been proposed to service targeted products and functions for e-commerce users, and figure out further research topics of e-commerce.