Mining Cross-platform User Behaviors for Demographic Attribute Inference

In this paper we seek to utilize the behavior information and the attribute labels of users from an auxiliary platform to help make attribute prediction. We propose a cross-platform demographic attribute inference model (CDAIM for short), in which we first learn the user representations with the behavior data, and then regulate the vectors of the same users from both platform to be close via a transfer function, and finally train a classifier with the feature vectors and attribute labels of all the users. We conduct extensive experiments on real datasets and the results show that our CDAIM outperforms the baselines.