Qu Yating, Ma Huahong, Wu Honghai, Zhang Kun, Deng Kaikai
School of Automotive and Rail Transportation, Luoyang Polytechnic, Luoyang 471099, China.
School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Entropy (Basel). 2022 Apr 1;24(4):495. doi: 10.3390/e24040495.
Identifying users across social media has practical applications in many research areas, such as user behavior prediction, commercial recommendation systems, and information retrieval. In this paper, we propose a multiple salient features-based user identification across social media (MSF-UI), which extracts and fuses the rich redundant features contained in user display name, network topology, and published content. According to the differences between users' different features, a multi-module calculation method is used to obtain the similarity between various redundant features. Finally, the bidirectional stable marriage matching algorithm is used for user identification across social media. Experimental results show that: (1) Compared with single-attribute features, the multi-dimensional information generated by users is integrated to optimize the universality of user identification; (2) Compared with baseline methods such as ranking-based cross-matching (RCM) and random forest confirmation algorithm based on stable marriage matching (RFCA-SMM), this method can effectively improve precision rate, recall rate, and comprehensive evaluation index (F1).
在社交媒体中识别用户在许多研究领域都有实际应用，比如用户行为预测、商业推荐系统和信息检索。在本文中，我们提出了一种基于多显著特征的跨社交媒体用户识别方法（MSF-UI），该方法提取并融合了用户显示名称、网络拓扑结构和发布内容中包含的丰富冗余特征。根据用户不同特征之间的差异，采用多模块计算方法来获取各种冗余特征之间的相似度。最后，使用双向稳定婚姻匹配算法进行跨社交媒体的用户识别。实验结果表明：（1）与单属性特征相比，整合了用户生成的多维度信息，优化了用户识别的通用性；（2）与基于排名的交叉匹配（RCM）和基于稳定婚姻匹配的随机森林确认算法（RFCA-SMM）等基线方法相比，该方法能有效提高准确率、召回率和综合评价指标（F1）。
