基于SVM的电子商务行为的性别判断

不同性别的用户对产品的看法与品位存在着差异,特别是在欣赏与时尚相关的产品上,性别对用户判断的影响显得尤为重要。根据电子商务中在线商品的浏览记录,采用支持向量机(support vector machines, SVM)对所选取的7个特征建立模型,并进行性别判断。经过模型分析和训练,准确率可达79.21%。同时讨论了网络购物与实体店购物的区别,并对SVM进行了核函数对比及其它性能的研究,从理论和实际应用上为核函数的选取和SVM的选用提供参考。

Research of gender prediciton based on SVM with E-commerce data

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.