基于用户签到的跨社交网络用户匹配

Cross-Social Network User Matching Based on User Check-in

跨社交网络用户匹配技术可以融合多平台用户数据,从而实现更多元的应用,现有基于签到的社交网络用户匹配研究,忽略了多源社交网络签到数据的失衡性,导致算法在真实数据集下匹配精度下降的问题。针对此问题,提出一种基于用户签到的跨社交网络用户匹配方法。通过网格聚类算法对用户签到数据进行粗粒度化和过滤,选择出潜在相关性强的签到数据;从这些签到数据中提取时空特征,计算出不同属性相似度;通过优化多属性相似度的权重分配,综合计算用户匹配分。在多组数据集上的实验结果表明,所提出方法在签到数据失衡情况下的有效性。

Cross-social network user matching technology can integrate multi-platform user data to realize more diverse applications.Existing research on social network user matching based on check-in ignores the imbalance of multi-source social network check-in data,which leads to a decrease of matching accuracy under real datasets.Aiming at this problem,this paper proposes a cross-social network user matching method based on user check-in.Firstly,the user check-in data is coarse-grained and filtered through grid clustering algorithm,and the check-in data with strong potential correlation is selected;then the spatiotemporal features are extracted from the check-in data,and the similarity of different attributes is calculated;finally,by optimizing the multi-attribute weight distribution of similarity,comprehensive calculation of user matching score is conducted.Experimental results on multiple datasets demonstrate the effectiveness of the proposed method in the case of unbalanced check-in data.