基于SVM的电子商务行为的性别判断

doi:10.6040/j.issn.1671-9352.1.2015.094

Abstract

Abstract: Different gender of Users have different view on products, particularly in appreciation of fashion related products, the gender influence is much important. This paper used seven characteristics choosed from online based e-commerce product browsing history data and used support vector machines(SVM)set model by these seven characteristics to predict users' gender. By analysing and training the model, accuracy of gender prediction reached up to 79.21%.While taking advantage of the problem, the paper discusseed the differences between online shopping and offline shopping and do the research about the kernel function of support vector machine and other performance, give the theory and practice reference for the selection of kernel functions and selection of support vector machine.

Key words: E-commerce, performance study, SVM, gender prediction

CLC Number:

TP393

PENG Qiu-fang, LIU Yang. Research of gender prediciton based on SVM with E-commerce data[J].JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(7): 74-80.

References

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