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基于三度影响力的社交好友推荐机制
作者：王名扬 贾冲冲 杨东辉 来源：《计算机应用》2015年第07期
摘要：针对社交网络中的好友推荐问题，提出了一种基于三度影响力理论的好友推荐算法。社交网络用户节点间的联系除了共同好友外，还存在其他不同长度的连通关系。该算法不再局限于仅以用户间共同好友的数量作为好友推荐的主要依据，而是在此基础上引入三度影响力理论进一步拓展关系连接，即把用户间距离三度以内的强连接用户都考虑进来，并通过为不同距离长度的连通关系分配相应的权重，实现好友关系强度的计算，来进行推荐。通过在新浪微博和Facebook社交网站上的实验结果表明，该算法比仅依据用户间共同好友数量的推荐算法在查准率和查全率上分别提高了约5%和0.8%，显著提升了社交平台好友推荐的效果，从而为社交平台改进推荐机制，以进一步增强用户体验提供了理论支撑。 关键词：社交网络；好友推荐；共同好友；三度影响力；强连接 中图分类号： TP301.6 文献标志码：A
Abstract： In view of the friend recommendation problem in social networks， a friend recommendation algorithm based on the theory of threedegree influence was proposed. The
relationships between social network users include not only the mutual friends， but also the other connecting relations with different lengths. By introducing the theory of threedegree influence， the algorithm took all the relationships within threedegree between users into account， while not only considering the number of mutual friends between users as the main basis of the friend
recommendation. By assigning corresponding weights to connections with different distances， the strength of friend relationship between users could be calculated， which would be used as the standard for recommendation. The experimental results on Sina microblog and Facebook show that the precision and recall rate of the proposed algorithm are improved by about 5% and 0.8%
respectively than that merely based on mutual friends， which indicates the better recommendation performance of the improved recommendation algorithm. It can be helpful for the social platform to improve the recommendation system and enhance the user experience.
Key words： social network； friend recommendation； mutual friend； threedegree influence； strong connection 0 引言
Web 2.0技术的发展带领大家走进了“大数据”时代和社交网络交互时代，近几年众多社交平台的出现就是这个时代鲜明的特征[1]。通过平台，用户可以更加方便地实现信息的分享与交流；同时，随着网络用户量的攀升所产生的海量数据造成了信息超载，使得用户在面对大量
基于三度影响力的社交好友推荐机制
