LBSN中位置信息与网络拓扑相融合的好友预测

Friends Prediction Based on Fusion of Topology and Location in LBSN

在基于位置的社会网络中,好友预测通常通过相似性标准来衡量用户间的相似性,然后将最相似的用户作为好友推荐给指定用户。传统的用户特征选取没有区分各个特征之间的差异,因而不能很好地代表用户的整体特征。提出了一种位置信息与社会网络拓扑相融合的好友预测方法。首先通过信息增益方法选取出更能代表用户整体特征的3个相关特征,然后对选取的特征进行融合,最后采用分类方法进行好友的预测。实验表明,提出的模型不依赖于具体的分类算法,并且预测性能优于多层好友模型。

In location based social networks, friends prediction usually predicts friends with some similarity metric, and recommends the most similar users to some user. Traditional selection methods of user features don＇ t consider the difference between different features, so they cannot represent the overall features of users. This paper proposed a friends prediction method based on fusion of location information and social topology. First, we selected three relative features that can represent the overall user feature with information gain, then fused the selected relative features, and finally predicted friends with classification method. The experiments show that the proposed method doesn＇t depend on the concrete classification method, and performs better than the multi-layer friend model.