DocCit2Vec: Citation Recommendation via Embedding of Content and Structural Contexts

The number of academic papers being published is increasing rapidly, and recommending sufficient citations to assist researchers in writing papers is a non-trivial task. Conventional recommendation approaches may not be optimal, as the recommended papers may already be known to the users or may be solely relevant to the surrounding context but not to other concepts discussed in the manuscript. In this study, we propose a novel embedding algorithm, namely DocCit2Vec, along with the new concept of structural context, to address the aforementioned issues. The proposed models are compared extensively with network-based, document-based, and combined approaches in experiments of citation recommendation and classification tasks. Three implications are concluded. First, the document-based methods demonstrated overwhelmingly superior performances for citation recommendation than the network-based methods, as the latter lack consideration of the word information. Second, DocCit2Vec exhibited significant improvement for citation recommendation among the document-based methods. Third, the ability to conduct classification tasks could be significantly enhanced by adding attention layer to DocCit2Vec.