产品(数学)
业务
采购
营销
大数据
层次分析法
质量(理念)
广告
聚类分析
电子商务
消费者行为
实证研究
计算机科学
数据挖掘
工程类
运筹学
数学
认识论
机器学习
哲学
几何学
作者
Li Jing,Li Yadong,Zhang Yanliang
标识
DOI:10.1145/3364335.3364336
摘要
Under the background of the volume of online stores with original brand increasing, the influencing factors of online consumers' purchase intention have been paid more and more attention. This paper collected online reviews for empirical analysis by constructing a big data mining framework based on chameleon clustering algorithm, and obtained hotspots about online reviews. Analytic hierarchy process is used to calculate the weights of factor. The results show that online reviews hotspots have several different degrees of impact on consumers' purchase intention. Among them, product style and material quality have the greatest impact on consumers' purchase intention, while logistics and customer service attitude, as health factors to stimulate consumers' purchase, although they can only maintain existing customers, they cannot increase the sales of product significantly.
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