基于点击流与PROMETHEE多属性决策法的电子商务消费者购买意愿预测

E-commerce Consumer Purchase Intention Prediction Based on Clickstream and PROMETHEE

在电子商务环境下,充分了解消费者的兴趣变化过程并预测其购买意愿是个性化推荐系统需要解决的问题,因此具有巨大潜在信息的点击流数据因其易获性及预测的准确性得到了广泛的研究与应用。为了挖掘用户点击行为所反映消费者的兴趣变化过程并预测消费者的购买意愿,通过点击流数据,基于兴趣漂移理论,采用偏好顺序结构评估法(Preference Ranking Organization Methods for Enrichment Evaluations,PROMETHEE)的多属性决策方法建立有效的模型进行测量与预测。结果建立了包括3个一级指标和8个二级指标构成的多会话消费者购买意愿评价体系,为消费者购买意愿预测提供了一种实用的评价方法。

Under the environment of e-commerce,fully understanding the consumer's interest change process and predicting their purchase intention are the problems that personalized recommendation systems need to solve.To solve this problem,clickstream data with huge potential information has been widely studied and applied due to its availability and accuracy of prediction.Therefore,in order to mine the consumer's interest change process reflected by the user's click behavior and predict the consumer's purchase intention,click stream data is used,based on interest drift theory,to establish an effective model for measurement and prediction on multi-attribute decisionmaking method(MADM)of preference ranking organization methods for enrichment evaluations(PROMETHEE).As a result,a multi-session consumer purchase intention evaluation system consisting of three primary indicators and eight secondary indicators is established,providing a practical evaluation method for consumer purchase intention prediction.