基于在线序列优化极限学习机的电子商务客户流失量预测模型

Predictions model of customer churn in E-commerce based on online sequential optimization extreme learning machine

为了提高电子商务客户流失量预测的准确性,针对单机处理模式无法有效预测海量电子商务客户流失量的难题,提出了在线序列优化极限学习机的电子商务客户流失量预测模型。首先通过云计算技术的Map/Reduce模式对电子商务客户流失量数据进行分割,得到多个训练子集,然后采用线序列优化极限学习机对电子商务客户流失量的每一个训练子集进行建模,并对训练子集的预测结果进行融合,得到电子商务客户流失量的最终预测结果,最后通过电子商务客户流失量算例进行模型的有效性进行验证。结果表明,该文模型提高了电子商务客户流失量的预测精度,而且减少了电子商务客户流失量建模的训练时间,大幅度提高了电子商务客户流失量预测速度。

In order to improve the customer churn prediction accuracy of E-commerce customer,and single machine model cannot effectively predict customer churn of massive E-commerce customers,this paper proposes a novel prediction model of customer churn in E-commerce based on online sequential optimization extreme learning machine. Firstly,the Map/Reduce model of cloud computing is used to segment the amount of customer churn in E-commerce,and multiple training subsets are obtained;secondly extreme learning machine is used to model each training subset of E-commerce customer churn,and the prediction results of training subsets are combined to get the final forecast results of customer churn in E-commerce;at last the validity of E-commerce customer churn prediction model is tested by example. The results show that the proposed model improves the prediction accuracy of customer churn in E-commerce,and the training time of E-commerce customer churn modeling has greatly reduced,improving the churn prediction speed of E-commerce customers.