TOPIC SEQUENCE EMBEDDING FOR USER IDENTITY LINKAGE FROM HETEROGENEOUS BEHAVIOR DATA

In social media, user identity linkage is a vital information security issue of identifying users' private information across multiple online social networks. With the popularity of behavior-rich social services, existing methods attempt to align users through encoding behaviors. However, most of the efforts suffer from the high variety and heterogeneity of behavior data across social networks, resulting in a limitation of modeling user intrinsic characteristics. To address the above issues, we focus on keyword-based topics to formulate user's variety behaviors for user identity linkage. In this paper, a novel Topic Sequence Embedding (TSeqE) method is proposed to embed contextual information of topics to represent users' intrinsic characteristics for identity linkage. Furthermore, we introduce a domain-adversarial training strategy to tackle the behavior heterogeneity problem. Our experiments on two real-world datasets demonstrate that TSeqE produces a significant improvement compared with several strong baselines.