PAVE: Personalized Academic Venue recommendation Exploiting co-publication networks.

Academic venues have risen beyond the imagination for the rapid development of information technology. It is necessary for researchers to acknowledge high quality and fruitful academic venues. However, the information overload problem in big scholarly data creates tremendous challenges for mining these venues and relevant information. In this work, we propose PAVE, a novel Personalized Academic Venue recommendation Exploiting co-publication networks. PAVE runs a random walk with restart model on a co-publication network which contains two kinds of associations, coauthor relations and author-venue relations. We define a transfer matrix with bias to drive the random walk by exploiting three academic factors, co-publication frequency, relation weight and researchers' academic level. PAVE is inspired from the fact that researchers are more likely to contact those who have high co-publication frequencies and similar academic levels. Additionally, in PAVE, we consider the difference of weights between two kinds of associations. Extensive experiments on DBLP data set demonstrate that, in comparison to relevant baseline approaches, PAVE performs better in terms of precision, recall, F1 and average venue quality.