基于三度影响力的社交好友推荐机制

doi:10.11772/j.issn.1001-9081.2015.07.1984

计算机应用 ›› 2015, Vol. 35 ›› Issue (7): 1984-1987.DOI: 10.11772/j.issn.1001-9081.2015.07.1984

基于三度影响力的社交好友推荐机制

王名扬¹, 贾冲冲¹, 杨东辉²

1. 东北林业大学信息与计算机工程学院, 哈尔滨 150040;
2. 东南大学经济管理学院, 南京 211189

收稿日期:2015-01-19 修回日期:2015-03-15 出版日期:2015-07-10 发布日期:2015-07-17
通讯作者: 王名扬(1980-),女,山东泰安人,副教授,博士,主要研究方向:数据挖掘、社交网络挖掘,wangmingyang@nefu.edu.cn
作者简介:贾冲冲(1990-),男,山东菏泽人,硕士研究生,主要研究方向:并行分布式及高性能计算; 杨东辉(1986-),男,山东济宁人,讲师,博士,主要研究方向:数据挖掘、推荐系统。
基金资助:
中央高校基本科研业务费专项资金资助项目(2572014DB05);国家自然科学基金资助项目(71473034);中国博士后科学基金面上基金资助项目(2012M520711)。

Social friend recommendation mechanism based on three-degree influence

WANG Mingyang¹, JIA Chongchong¹, YANG Donghui²

1. College of Information and Computer Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China;
2. School of Economics and Management, Southeast University, Nanjing Jiangsu 211189, China

Received:2015-01-19 Revised:2015-03-15 Online:2015-07-10 Published:2015-07-17

摘要/Abstract

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

Abstract:

In view of the friend recommendation problem in social networks, a friend recommendation algorithm based on the theory of three-degree 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 three-degree influence, the algorithm took all the relationships within three-degree 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, three-degree influence, strong connection

中图分类号:

TP301.6

王名扬, 贾冲冲, 杨东辉. 基于三度影响力的社交好友推荐机制[J]. 计算机应用, 2015, 35(7): 1984-1987.

WANG Mingyang, JIA Chongchong, YANG Donghui. Social friend recommendation mechanism based on three-degree influence[J]. Journal of Computer Applications, 2015, 35(7): 1984-1987.

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基于三度影响力的社交好友推荐机制

Social friend recommendation mechanism based on three-degree influence

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