Optimizing MAP in Joint Multi-Relational Models for Recommendations in the Academic Network∗

In this paper, we target at four specific recommendation tasks in the aca-demic environment: the recommendation for author coauthorships, paper ci-tation recommendation for authors, paper citation recommendation for pa-pers, and publishing venue recommendation for author-paper pairs. Differ-ent from previous work which tackles each of these tasks separately while neglecting their mutual effect and connection, we propose a joint multi-relational model that can exploit the latent correlation between relations and solve several tasks in a unified way. Moreover, for better ranking purpose, we extend the work maximizing MAP over one single tensor, and make it applicable to maximize MAP over multiple matrices and tensors. Experi-ments conducted over two real world data sets demonstrate the effectiveness of our model: 1) improved performance can be achieved with joint modeling over multiple relations; 2) our model can outperform three state-of-the art algorithms for several tasks