Paper recommendation based on heterogeneous network embedding

Researchers face millions of research papers on various digital libraries. Therefore, finding relevant research work that meets the preferences of a researcher is a challenging task. Hence, different paper recommendation models have been proposed to address this issue. However, these models lack in exploiting prominent information factors, namely: papers’ citations proximity, authors’ information, papers’ topical relevance, venues’ information, researchers’ preference dynamics, and labels information to produce quality recommendations. Additionally, these models encounter problems such as cold start papers and data sparsity. To overcome these problems, this paper presents a weighted probabilistic paper recommendation model termed as PR-HNE, which jointly learns researchers’ and papers’ dynamics by encoding information from six information networks into a joint latent space. Specifically, it captures papers’ citation proximity, authors’ collaboration proximity, venues’ information, labeled information, and topical relevance to generate personalized paper recommendations. Compared to state-of-the-art models, the results generated by PR-HNE over publicly available datasets prove 4% and 6% improvement in Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) metrics, respectively. Further, in the cold-start papers problem, the proposed model produced 8% better recall score than its counterparts.