作　　者： ; ;

机构地区： 广东工业大学计算机学院

出　　处： 《广东工业大学学报》 2020年第3期1-8,共8页

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 算法 因果网络

领　　域： [] []