融合多类时空轨迹特征的跨网络用户身份识别

Cross-network User Identification Based on Multiple Spatio-Temporal Trajectory Features

随着位置社交网络的蓬勃发展,用户移动行为数据得到极大丰富,推动了基于时空数据的身份识别问题的相关研究。跨位置社交网络的用户身份识别,强调学习不同平台时空序列间的相关性,旨在发现同一用户在不同平台的注册账号。为解决现有研究面临的数据稀疏、低质量和时空不匹配问题,提出了一种融合双向时空依赖和时空分布的识别算法UI-STDD。该算法主要包含3个模块:时空序列模块通过结合成对注意力的双向长短时记忆网络来刻画用户移动模式;时间偏好模块从粗、细两个粒度定义用户个性化模式;空间位置模块挖掘位置点的局部和全局信息,量化空间邻近性。基于上述模块得到的用户轨迹对特征,UI-STDD利用多层前馈网络判断跨网络的两个账户是否对应于现实中的同一个人。为验证UI-STDD的可行性和有效性,在3组公开的数据集上进行了实验。实验结果表明,所提算法能够提高基于时空数据的用户身份识别率,F1值平均高于最优对比方法10%以上。

With the flourishing of location-based social networks, users’ mobile behavior data has been greatly enriched, which promotes the research on user identification based on spatio-temporal data.User identification in cross-location social networks emphasizes learning the correlation between time and space sequences of different platforms, aiming at discovering the accounts registered by the same user on different platforms.In order to solve the problems of data sparsity, low quality and spatio-temporal mismatch faced by existing researches, a recognition algorithm UI-STDD combining bidirectional spatio-temporal dependence and spatio-temporal distribution is proposed.The algorithm mainly consists of three modules: the space-time sequence module is combined with the bidirectional long short-term memory network of paired attention to describe user movement patterns;the time preference module defines the user personalized mode from coarse and fine granularity;the spatial location module mines local and global information of location points to quantify spatial proximity.Based on the user trajectory pair features obtained by the above modules, a multi-layer feedforward network is used in UI-STDD to distinguish whether two accounts across the network corres-pond to the same person in real life.To verify the feasibility and effectiveness of UI-STDD,experiments are carried out on three publicly available datasets.Experimental results show that the proposed algorithm can improve the user identification rate based on spatio-temporal data, and the average F1 value is more than 10% higher than the optimal comparison method.