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

doi:10.12052/gdutxb.200029

Abstract

Abstract: 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 decision-making 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.

Key words: preference ranking organization methods for enrichment evaluations (PROMETHEE), consumer interest drift, purchase intention, clickstream data, session

CLC Number:

F713.3

He Rui-chao, Liu Hong-wei, Gao Hong-ming, Fan Meng-ting, Zhan Ming-jun. E-commerce Consumer Purchase Intention Prediction Based on Clickstream and PROMETHEE[J].Journal of Guangdong University of Technology, 2020, 37(06): 32-40.

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