摘要
Abstract
Based on consumer clickstream data and network structure,this paper uses Temporal Exponential Random Graph Model(TERGM)and consumer clickstream data to construct a consumer dynamic co-purchase network,identifies the key variables of consumer co-purchase behavior from the dimensions of product click times,relative browsing time,positive comments,negative comments and product penetration,and compares it with Exponential Random Graph Model(ERGM).The results show that product relative browsing time,praise number and product penetration promote the occurrence of consumers'co-purchase behavior,while the number of product clicks will reduce the possibility of consumers'co-purchase.TERGM model is suitable for the network analysis of consumers'co-purchase behavior,and the fitting effect is better than ERGM,which verifies the applicability of the TERGM model to consumer co-purchase behavior.This paper suggests that the network structure perspective should be added in the implicit feedback of clickstream to study the impact on the formation of co-purchase network,which provides a useful reference for the optimal design of recommendation system.关键词
共同购买/消费者点击流数据/网络分析/指数随机图模型/时间指数随机图模型Key words
co-purchase/consumer clickstream data/network analysis/exponential random graph model/time exponential random graph model分类
信息技术与安全科学
