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Due to the availability of references of research papers and the richinformation contained in papers, various citation analysis approaches have beenproposed to identify similar documents for scholar recommendation. Despite ofthe success of previous approaches, they are, however, based on co-occurrenceof items. Once there are no co-occurrence items available in documents, theywill not work well. Inspired by distributed representations of words in theliterature of natural language processing, we propose a novel approach tomeasuring the similarity of papers based on distributed representations learnedfrom the citation context of papers. We view the set of papers as thevocabulary, define the weighted citation context of papers, and convert it toweight matrix similar to the word-word cooccurrence matrix in natural languageprocessing. After that we explore a variant of matrix factorization approach totrain distributed representations of papers on the matrix, and leverage thedistributed representations to measure similarities of papers. In theexperiment, we exhibit that our approach outperforms state-of-theartcitation-based approaches by 25%, and better than other distributedrepresentation based methods.

Paper2vec: Citation-Context Based Document Distributed Representation for Scholar Recommendation