Scientific article recommendation problem deals with recommending similar scientific articles given a query article. It can be categorized as a content based similarity system. Recent advancements in representation learning methods have proven to be effective in modeling distributed representations in different modalities like images, languages, speech, networks etc. The distributed representations obtained using such techniques in turn can be used to calculate similarities. In this paper, we address the problem of scientific paper recommendation through a novel method which aims to combine multimodal distributed representations, which in this case are: 1. distributed representations of paper's content, and 2. distributed representation of the graph constructed from the bibliographic network. Through experiments we demonstrate that our method outperforms the state-of-the-art distributed representation methods in text and graph, by 29.6% and 20.4%, both in terms of precision and mean-average-precision respectively.
Gupta and Varma REF proposed a global scientific paper recommendation approach, which combines distributed representations of the graph constructed from the bibliographic network and content of papers.
Scientific Article Recommendation by using Distributed Representations of Text and Graph
WWW
Computer Science