User recommendation based on cross-platform online social networks - Jian PENG; Tuntun WANG; Yu CHEN; Tang LIU; Wenzheng XU (Tongxin xuebao)
In the field of online social networks on user recommendation,researchers extract users’ behaviors as much as possible to model the users.However,users may have different likes and dislikes in different social networks.To tackle this problem,a cross-platform user recommendation model was proposed,users would be modeled all-sided.In this study,the Sina micro blog and the Zhihu were investigated in the proposed model,the experimental results show that the proposed model is competitive.Based on the proposed model and the experimental results,it can be known that modeling users in cross-platform online social networks can describe the user more comprehensively and leads to a better recommendation.