Multi-source User Attribute Inference based on Hierarchical Auto-encoder.

With the rapid development of Online Social Networks (OSNs), it is crucial to construct users' portraits from their dynamic behaviors to address the increasing needs for customized information services. Previous work on user attribute inference mainly concentrated on developing advanced features/models or exploiting external information and knowledge but ignored the contradiction between dynamic behaviors and stable demographic attributes, which results in deviation of user understanding To address the contradiction and accurately infer the user attributes, we propose a Multi-source User Attribute Inference algorithm based on Hierarchical Auto-encoder (MUAI-HAE). The basic idea is that: the shared patterns among the same individual's behaviors on different OSNs well indicate his/her stable demographic attributes. The hierarchical autoencoder is introduced to realize this idea by discovering the underlying non-linear correlation between different OSNs. The unsupervised scheme in shared pattern learning alleviates the requirements for the cross-OSN user account and improves the practicability. Off-the-shelf classification methods are then utilized to infer user attributes from the derived shared behavior patterns. The experiments on the real-world datasets from three OSNs demonstrate the effectiveness of the proposed method.