Recommendation in Academia: A joint multi-relational model

Abstract

In this paper, we target at four specific recommendation tasks in the academic environment: the recommendation for author coauthorships, paper citation recommendation for authors, paper citation recommendation for papers, and publishing venue recommendation for author-paper pairs. Different 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. Experiments 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.