基于概念格和随机游走的社交网朋友推荐算法

A Friends Recommendation Algorithm Based on Formal Concept Analysis and Random Walk in Social Network

在社交网络朋友推荐上,现有方法通过用户注册的共同属性或者用户共同邻居来对用户进行朋友推荐,由于缺乏对用户之间关系的深入的挖掘,推荐精度不高。采用概念格从数据中挖掘知识,利用用户特征属性和社交网络图建立概念格,提出了弹性随机游走方法 SRWR,并在此基础上用概念格知识指导随机游走,提出了融合概念格和随机游走的FCASRWR方法,度量了用户之间的相似性,算法最终根据相似度进行朋友推荐。实验采用Facebook的真实数据集,采用AUC和精确度评价指标,实验结果表明,该方法比目前主流的方法在指标上有较大提高,验证了方法的准确性。

Formal concept analysis was leveraged to acquire knowledge in data. Two concept lattices were built from the user feature attributes and social networking diagram. The random walk method SRWR was proposed and then the FCASRWR method was put forward with the guidance of concept lattice. The FCASRWR method measured the similarity between users,and recommended friends according to the similarity algorithm to users. The Experiments of using Facebook＇s real datasets showed that the proposed method has a better performance and proved the accuracy of the method.