基于因果模型的社交网络用户购物行为研究  

A Research on Users’Shopping Behaviors in Social Network Based on Causal Model

作　　者：郝志峰[1,2] 黎伊婷 蔡瑞初 曾艳[1] 乔杰 Hao Zhi-feng;Li Yi-ting;Cai Rui-chu;Zeng Yan;Qiao Jie(School of Computers,Guangdong University of Technology,Guangzhou 510006,China;College of Mathematics and Big Data,Foshan University,Foshan 528000,China)

机构地区：[1]广东工业大学计算机学院,广东广州510006 [2]佛山科学技术学院数学与大数据学院,广东佛山528000

出　　处：《广东工业大学学报》2020年第3期1-8,共8页Journal of Guangdong University of Technology

基　　金：国家自然科学基金资助项目(61876043);广东省自然科学基金资助项目(2014A030306004,2014A030308008);NSFC-广东联合基金资助项目(U1501254);广东特支计划资助项目(2015TQ01X140);广州市珠江科技新星资助项目(201610010101);广州市科技计划项目(201902010058)。

摘　　要：社交网络用户的购物行为体现用户在社交影响下自身物质需求和社交需求的意愿,是社交网络营销的重要研究内容。传统的网络购物行为分析仅关注用户行为间的相似度,忽略了用户的社交需求及同伴行为的影响。对此,结合反从众理论和社交需求特性,对用户购物行为进行特征构建;其次,针对社交网络用户数据不完全观察特性,提出了基于快速因果推断(Fast Causal Inference,FCI)的用户行为因果机制发现算法;最后,基于模型的实验分析和实证分析验证了模型因果机制的合理性。Shopping behaviors in the social network can reflect users’willingness to meet their material needs and social needs under the influence of social interaction,which is an important research in social network marketing.The traditional analysis of online shopping behavior only focuses on the similarity between users’behaviors while ignoring the influence of users’social needs and peer behaviors.For that,the features of users’shopping behavior are constructed by combining anti-conformity theory and social needs.Secondly,aiming at the incomplete observation of user data in social network,a causal mechanism discovery algorithm for users’behaviors based on Fast Causal Inference(FCI)is proposed.Finally,the rationality of the causal mechanism of our model is verified based on the experimental analysis and empirical analysis.

关 键 词：网络购物 社交行为 反从众 FCI算法 因果网络 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]