  Citation based approaches have seen good progress for recommending research
papers using citations in the paper. Citation proximity analysis which uses the
in-text citation proximity to find relatedness between two research papers is
better than co-citation analysis and bibliographic analysis. However, one
common problem which exists in each approach is that paper should be well
cited. If documents are not cited properly or not cited at all, then using
these approaches will not be helpful. To overcome the problem, this paper
discusses the approach Virtual Citation Proximity (VCP) which uses Siamese
Neural Network along with the notion of citation proximity analysis and
content-based filtering. To train this model, the actual distance between the
two citations in a document is used as ground truth, this distance is the word
count between the two citations. VCP is trained on Wikipedia articles for which
the actual word count is available which is used to calculate the similarity
between the documents. This can be used to calculate relatedness between two
documents in a way they would have been cited in the proximity even if the
documents are uncited. This approach has shown a great improvement in
predicting proximity with basic neural networks over the approach which uses
the Average Citation Proximity index value as the ground truth. This can be
improved by using a complex neural network and proper hyper tuning of
parameters.
