Research on Consumers'Dynamic Co-purchase Network Based on Clickstream Data
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.
co-purchaseconsumer clickstream datanetwork analysisexponential random graph modeltime exponential random graph model
国家科技期刊平台
NSTL
万方数据
