To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication proﬁles of the researchers themselves. While using such a publication citation network has been shown to enhance
performance, the network is often sparse, making recommendation
difﬁcult. To alleviate this sparsity, we identify “potential citation papers” through the use of collaborative ﬁltering. Also, as different logical sections of a paper have different signiﬁcance, as a secondary contribution, we investigate which sections of papers can be leveraged to represent papers effectively.
On a scholarly paper recommendation dataset, we show that recommendation accuracy signiﬁcantly outperforms state-of-the-art
recommendation baselines as measured by nDCG and MRR, when we discover potential citation papers using imputed similarities via
collaborative ﬁltering and represent candidate papers using both the full text and assigning more weight to the conclusion sections.