关键词: 社会网络, Least mean square （LMS）算法, 相似度度量, Madaline网络, 文本挖掘

Abstract: 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%.

Key words: Social Network Site, Least Mean Square (LMS )algorithm, similarity measured, Madaline Network, Text Mining