Subspace Embedding Based New Paper Recommendation

As huge numbers of academic papers are published every year, it is critical to be able to recommend high quality papers. The typical evaluation method for papers is to use citation information, which however is not applicable to new papers. To address such a shortcoming, in this paper, we consider a novel perspective on the association between the content difference of a paper, with respect to other papers, and its innovation. Since innovation has often domain-specific characteristics and forms, we introduce the concept of subspace to describe the commonly recognized aspects of paper contents, namely background, methods and results. A set of expert rules are formalized to annotate the differences between papers, based on which a twin-network is proposed for learning the embeddings of papers in different subspaces. A series of empirical studies show that there are clear correlations between a paper influence and its difference with others in those subspaces. The results also show the characteristics of innovation in different scientific disciplines. To take into account information about academic networks for paper recommendation, we propose a graph convolutional neural method to combine the paper content with other related elements, where user interests and academic influences are modeled asymmetric. Experimental results on real datasets show that our method is more effective than other baseline methods for new paper recommendation. We also discuss the characteristics of scientific disciplines and authors to show the effectiveness of modeling the asymmetric user interests and influences. Finally, we verify the reusability of our method on a patent dataset. The results show that it is also applicable to academic data with low-resource features.