基于用户话题偏好的社会网络二级人脉推荐

社会网络（SNS）用户的社交圈和人脉关系研究多采用图论的知识，对社会网络关系图的节点和边进行探讨，没有考虑到用户自身的兴趣偏好，因此提出了一种基于用户话题偏好的二级人脉推荐方法。利用文本挖掘的相关技术和最小均方误差（LMS）算法，把抓取到的用户话题数据合理地转化为用户话题偏好特征向量，用相似度度量方法来计算用户之间的相似度，以确定与用户话题偏好最相近的用户集，并完成用户的二级好友推荐。实验表明，推荐的二级好友采纳率达到70%。

Second-level contacts recommendation of social network service based on subjects of users' preference

The interpersonal contacts of the Social Network Service(SNS) customers are often researched based on the information of the graph theory.The preference of the customers themselves is often ignored,when discussing the nodes and the edges of the relationship graph of SNS.Thus,a second-level interpersonal contacts method based on the subjects of users’ preference was proposed in this paper.Utilizing text mining technology and the Least Mean Square(LMS) algorithm,the authors transformed the subjects of users’ preference into feature vectors reasonably.In order to ensure the set of the customers similar to the subjects of users’ preference and complete the second-level recommendation of the customers,the similarity of the customers was computed with the similarity measurement.The experimental results show that the recommendation accuracy for good friend of this algorithm is very high.The acceptance rate of the recommended good friends is 70%.