Abstract: 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.

Key words: User identification, Spatio-Temporal data, Mobile mode, Time preference, Long short-term memory