基于跨平台的在线社交网络用户推荐研究

User recommendation based on cross-platform online social networks

在社交网络用户推荐研究领域,通过提取用户的行为模式对其进行好友推荐。但是用户的行为是多样性的,在不同的社交平台,用户可能有不同的行为模型。提出跨平台用户推荐模型,同时对用户相关的所有社交网络平台进行建模,最后将用户在所有平台的行为模式进行融合。基于真实的新浪微博数据集和知乎数据集,通过一系列对比实验证明,跨平台用户推荐模型可以更加全面准确地刻画用户行为,更好地进行用户推荐。

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.