社交网络中基于分类属性的好友推荐

社交网络经常通过掌握的用户信息来对其进行好友推荐。这种好友推荐带来了技术挑战，现有的好友推荐技术并不能有效解决该问题。为了应对这种技术挑战，拟提出基于分类属性的好友推荐算法。通过机器学习的手段，分析出不同类型的属性对用户行为的贡献度不同，将其进行分类处理。基于该分类，提出的算法可以在掌握用户基本资料以及近期行为的基础上，搜索出与之相关性更强的好友或能够引发其兴趣点的商品，用来快速、准确、全面地得到用户与其好友之间亲疏程度排序及分类的结果。实验结果证明了所提出方法的有效性及高效率。

Friends recommendation of social network based on property

Social network recommends friends according to the information of the users. This problem brings technical challenges. Current technologies cannot solve this problem effectively. Facing the challenges, this paper proposes a recommendation algorithm based on the classification of properties. It analyzes the contributions of different types of properties to users’ behavior by machine learning and classifies the types of results. Based on the classifications, it proves the algorithm can find more relevant friends or the merchandise which are more likely to arouse users’ interest so as to acquire the classification as well as the degree of closeness between users and their friends more correctly, rapidly and comprehensively. This paper validates the effectiveness and the efficiency of proposed algorithms with extensive experiments.