计算机科学 ›› 2014, Vol. 41 ›› Issue (9): 115-118.doi: 10.11896/j.issn.1002-137X.2014.09.022
潘果,徐雨明
PAN Guo and XU Yu-ming
摘要: 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。
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