基于用户潜在特征的社交网络好友推荐方法  

Friend Recommendation Method Based on Users' Latent Features in Social Networks

作　　者：肖迎元 张红玉 

机构地区：[1]天津市智能计算及软件新技术重点实验室,天津300384 [2]天津理工大学计算机科学与工程学院,天津300384

出　　处：《计算机科学》2018年第3期218-222,252,共6页Computer Science

基　　金：国家自然科学基金重大研究计划(91646117);国家自然科学基金(61170174);天津市自然科学基金(17JCYBJC15200);天津市科技特派员项目(16JCTPJC53600)资助

摘　　要：随着Facebook、Twitter、微博等社交网站的迅速普及,好友推荐系统逐渐成为各大社交网站的重要组成部分。好友推荐系统通过主动为用户推荐新的潜在好友来有效地扩大用户的社交圈规模并改善用户的社交体验,因而受到了广泛关注。然而,如何针对用户的个性化需求,为用户推荐真正意义上的好友,一直是个性化好友推荐的难点之一。对此,提出一种基于用户潜在特征的社交网络好友推荐方法(SNFRLF)。首先,通过隐语义模型挖掘用户的潜在属性特征;然后,通过用户的潜在特征计算用户间的相似度;最后,将计算得到的相似度引入到随机游走模型中以获得好友推荐列表。实验结果表明,文中所提好友推荐方法较已有的好友推荐方法在性能上有显著提升。With the popularity of social networks,such as Facebook,Twitter and Microblog,friend recommendation systems have gradually become an important part of social networks.Friend recommendation systems effectively expand the scale of user's social circle and improve user's social experience by actively recommending new potential friends for users,thus receiving widespread attention.However,how to personalize the user's needs and recommend realfriends to users has been one of the difficulties for personalized friend recommendation.This paper presented a social networking friend recommendation method based on users' latent features,called SNFRLF.SNFRLF first leverages latent factor model to mine users' latent features,and then calculates the similarity between users by means of users' latent features.Finally,the similarity is introduced into the random walk model to get a recommended list.The experimental results show that the proposed method significantly outperforms the existing friend recommendation methods.

关 键 词：好友推荐 社交网络 隐语义模型 随机游走 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]