基于K-Means聚类的消费者直播购物偏好研究

Research on Consumers’ Preference for Live Shopping Based on K-Means Clustering

本文针对消费者直播购物行为数据展开分析,采用K-means算法进行聚类,将具有相似购买行为的样本聚为一组,得到三种类别的直播购物偏好风格,进一步分析了各个偏好风格的群体特征,并为电商平台实施直播互动提供启示。

Based on the analysis of consumers’ live shopping behavior data, this paper uses K-means algorithm for clustering, gathers samples with similar purchasing behaviors into a group, obtains three types of live shopping preference styles, further analyzes the group characteristics of each preference style, and provides inspiration for e-commerce platforms to implement live interaction.