Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (2): 76-84.DOI: 10.3778/j.issn.1002-8331.2203-0581

• Theory, Research and Development • Previous Articles     Next Articles

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

DAI Jun, MA Qiang   

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China
  • Online:2023-01-15 Published:2023-01-15



  1. 西南科技大学 信息工程学院,四川 绵阳 621010

Abstract: 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.

Key words: cross-social network, user matching, check-in similarity

摘要: 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。

关键词: 跨社交网络, 用户匹配, 签到相似度