【摘要】 现今社交媒体是建立社交联系的重要媒介,好友推荐对于扩展人们的关系网络起到至关重要的作用,准确的用户特征提取和分析是社交网络中好友推荐的关键.传统的好友推荐方法一般都是根据部分用户属性信息或行为信息进行推荐,所以对用户特征的描述不完整,推荐的效率和准确率远非预期.提出基于用户语义行为和社交关联的推荐模型应用于社交媒体平台上的好友推荐.为了获得准确的预测,使用LDA(Latent Dirichlet Allocation)对语义信息进行主题建模,得到基于主题的用户语义行为特征表达;使用DeepWalk算法对用户社交关联网络图进行特征提取,得到准确的社交关联特征表达;使用反向传播神经网络来预测用户潜在的社交关联,为用户精准推荐好友.该模型实现了利用用户语义行为和社交关联预测用户潜在的社交关联,可以根据潜在社交关联进行精准的好友推荐.更多还原

【Abstract】 Nowadays,people usually establish social links on social media platforms,in which case friend recommendation plays a crucial role in expanding their communication.Accurate user feature extraction and analysis is the key to friend recommendation in social networks.Traditional methods of friend recommendation usually use some of users’ attribute information or behavior information which leads to the incomplete feature extraction,and the efficiency and accuracy of the recommendation are far from the expectation.We propose a recommendation model based on users’ semantic behavior and social links applied to the friend recommendation on social media platforms.In order to obtain accurate predictions,LDA(Latent Dirichlet Allocation)is used to deal with the semantic information to get topic-based representation of semantic behavior.DeepWalk algorithm is used to extract features of the graph which consists of social links to obtain accurate feature representations.The back propagation neural network is processed to predict the potential social links among users and recommend friends accurately.The model predicts thepotential social links considering the semantic behavior and social links,and then recommends friends accurately with the potential social links.更多还原