  Due to the availability of references of research papers and the rich
information contained in papers, various citation analysis approaches have been
proposed to identify similar documents for scholar recommendation. Despite of
the success of previous approaches, they are, however, based on co-occurrence
of items. Once there are no co-occurrence items available in documents, they
will not work well. Inspired by distributed representations of words in the
literature of natural language processing, we propose a novel approach to
measuring the similarity of papers based on distributed representations learned
from the citation context of papers. We view the set of papers as the
vocabulary, define the weighted citation context of papers, and convert it to
weight matrix similar to the word-word cooccurrence matrix in natural language
processing. After that we explore a variant of matrix factorization approach to
train distributed representations of papers on the matrix, and leverage the
distributed representations to measure similarities of papers. In the
experiment, we exhibit that our approach outperforms state-of-theart
citation-based approaches by 25%, and better than other distributed
representation based methods.
