Scalable Heterogeneous Transfer Ranking.

In this paper, we propose to study the problem of heterogeneous transfer ranking , a transfer learning problem with heterogeneous features in order to utilize the rich large-scale labeled data in popular languages to help the ranking task in less popular languages. We develop a large-margin algorithm, namely LM-HTR, to solve the problem by mapping the input features in both the source domain and target domain into a shared latent space and simultaneously minimizing the feature reconstruction loss and prediction loss. We analyze the theoretical bound of the prediction loss and develop fast algorithms via stochastic gradient descent so that our model can be scalable to large-scale applications. Experiment results on two application datasets demonstrate the advantages of our algorithms over other state-of-the-art methods.