细微粒度视域下国内外跨社交网络用户对齐研究综述

Review of cross social network user alignment from the fine-grained perspective at home and abroad

针对多源复杂网络用户管理所面临的冷启动、信息孤岛问题难以满足企业在不同社交网络中识别同一用户的困境,传统的仅使用单一维度特征提取源的跨社交网络用户对齐方法难以实现迅速、精准的用户识别,而使用细微粒度视域下的多维特征提取源可以有效耦合各类用户信息的优势以提升识别效率和精准度。论文首先以国内外跨社交网络用户对齐的相关文献为研究对象进行系统性分析,从细微粒度视域出发对文献主要内容进行汇总与梳理;然后逐一细化地对比分析以总结现有不同研究所采用的特征提取源之间的差异、优势和桎梏;最后从隐私数据集获取、多维数据集成和多源社交网络数据耦合3个方面来对未来用户对齐领域中的研究前沿方向与研究深入挖掘点作出展望。

In view of the cold start and information silo problems faced by the user management of multi-source complex networks,it is difficult for enterprises to meet the dilemma of identifying the same user in different social networks.It is also difficult to achieve rapid and accurate user recognition with the traditional cross social network user alignment method that only uses a single dimension feature extraction source.The use of multidimensional feature extraction sources from the fine-grained perspective can effectively couple the advantages of various users’information to improve recognition efficiency and accuracy.The paper first conducted a systematic analysis of relevant literature on cross social network user alignment at home and abroad.Starting from the fine-grained perspective,the main content of the literature was summarized and sorted out.Then,a detailed comparative analysis was conducted one by one to clarify the differences,advantages,and constraints between different feature extraction sources used in existing research.Finally,the data were obtained from a private dataset.From the perspectives of multidimensional data integration and multi-source social network data coupling,we provide prospects for the cutting-edge research directions and in-depth exploration in the field of user alignment in the future.