Prediction of Online Shoppers’ Purchasing Intention Using Support Vector Machines(SVM)

Li Zhu

doi:10.47297/wspciWSP2516-252730.20210506

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Prediction of Online Shoppers’ Purchasing Intention Using Support Vector Machines(SVM)

Volume 5, Issue 6

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Prediction of Online Shoppers’ Purchasing Intention Using Support Vector Machines(SVM)
Prediction of Online Shoppers’ Purchasing Intention Using Support Vector Machines(SVM)
Taizhou Institute of Science & Technology, Nanjing University of Science &Technology
Vol. 5, Issue 6, Pages: 185-194(2021)
Published： 2021 ，
DOI：10.47297/wspciWSP2516-252730.20210506
Accepted：

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LI ZHU. (2021). Prediction of Online Shoppers’ Purchasing Intention Using Support Vector Machines(SVM). Creativity and Innovation, 5(6), 185-194..
DOI：

LI ZHU. (2021). Prediction of Online Shoppers’ Purchasing Intention Using Support Vector Machines(SVM). Creativity and Innovation, 5(6), 185-194.. DOI： 10.47297/wspciWSP2516-252730.20210506.

摘要

Abstract

SVM is based on VC dimension theory and knot in statistical learning theory Based on the principle of structural risk minimization

according to the limited sample information in the module Seek the best compromise between the complexity of type and learning ability to obtain the most Good promotion ability. In this paper

the support vector machine classifier is used to analyze online shopper behavior characteristic data

and the user’s browsing and session information and customer information characteristics during the visit to the website are extracted as input to the support vector machine classifier. In the process

the optimal kernel function SBF for the classification result of the data set was selected and its parameters were optimized to obtain the optimal regularization (cost) parameter C and parameter gamma. The final classification results show that using the optimized SVM classifier can better use the user behavior characteristics to predict whether the user has a purchase behavior.

关键词

Keywords

Support Vector MachinesOnline Shoppers’PurchasingPolicy decision

references

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