基于隐朴素贝叶斯模型的社会关系推荐

Hidden nave Bayesian model for social relation recommendation

基于共邻用户属性的社会关系推荐算法是社会网络分析关系预测领域的热点研究方向。提出了一种基于隐朴素贝叶斯(hidden nave Bayesian,HNB)模型的用户关系推荐算法。该算法通过分析属性之间的依赖性对问题建模,从中度量共邻用户之间关系对推荐用户对之间的贡献和影响,然后对所有候选推荐关系计算其相似度并进行排序,并把模型推广到CN、AA和RA三种关系推荐算法中。在真实网络数据集上的实验结果表明,所提出的算法比目前的基准方法和朴素贝叶斯方法具有更高的AUC值。此外,算法能够发现具备不同拓扑结构属性的网络对推荐精度有着线性的影响。

Relation recommendation based on common neighbors＇ property is a hot research branch of link prediction in social network analysis. This paper proposesd a new measure of relation recommendation by introducing a hidden naive Bayesians （HNB） classification model, which model the task by analyzing the dependency among properties and incorporates this idea to measure the influence and contribution among common neighbors. Then built a ranking model to learn the highest similarity as- sociated with each candidate pair by maximizing the likelihood of relationship building and extended the model to CN, AA and RA similarity-based recommendation algorithms. Experimental evaluation by AUC on real social networks proved that the pro- posed model can achieve a better result than some baseline and LNB. Finally, it also discovered that attributes with different network topologies recommended precision linear effects.