摘要
在线社交网络已成为用户交互和分享信息的流行的互联网平台。其中,为用户推荐朋友是在线社交网络的一项重要服务。一方面,目前在线社交网络通常基于社会图的局部特性为用户推荐朋友(即,用户间的共同朋友数目)。这种方法仅使用路径长度为2的局部结构信息,没有充分利用社会图中各种不同长度的路径及其它信息。另一方面,基于社会图全局特性的在线社交网络朋友推荐方法虽然侦测了整个社会图的结构,但是对于大规模的在线社交网络来说,这类方法的计算成本相当高。为此,本文提出了一个新的在线社交网络朋友推荐方法。它根据"小世界"假说,随机游走有限范围内的所有路径,为用户提供了既快速又准确的朋友推荐。本文使用两个真实的在线社交网络的数据集对新方法进行评估。实验结果显示提出的方法显著增加了在线社交网络朋友推荐的准确性。
Online social networks （OSNs） have become popular, which provide users with a new communication and information sharing Internet platform. In OSNs, Recommending friends to registered users is a crucial task. On the one hand, OSNs often recommend friends for users based on local-based features of the social graph（i, e. based on the number of common friends that tho users share）. This method considers only pathways of lenght 2 between users and does not exploit all different length paths of the network and other information. On the other hand, there are global-based approaches of friend recommendation in OSNs which detect all pathway structures of the network. But its computation cost is quite high for large scale OSNs. In this paper, we propose a new approach of friend recommendation in OSNs which traverses all the paths of limited length through randomwalk based on ＂small world＂ hypothesis. This new method provides users with both fast and accurate friend recommendation in OSNs. To demonstrate practical applicability of the new aproach, we use two real world datasets to evaluate our novel approach. Experimental results showed the approach can significantly improve the accruracy of friend recommendation in OSNs.
出处
《系统工程》
CSSCI
CSCD
北大核心
2013年第2期47-54,共8页
Systems Engineering
基金
江苏省现代教育技术研究2012年度技术应用重点课题(2012-R-22749)
关键词
朋友推荐
在线社交网络
随机游走
Friend Recommendation
Online Social Networks
Random Walk
作者简介
俞琰（1972-），女，浙江人，研究方向：社会网络，数据挖掘；
邱广华（1964-），男，福建人，宾州州立大学终身副教授．博士生导师，研究方向：服务科学。
