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Citation based approaches have seen good progress for recommending researchpapers using citations in the paper. Citation proximity analysis which uses thein-text citation proximity to find relatedness between two research papers isbetter than co-citation analysis and bibliographic analysis. However, onecommon problem which exists in each approach is that paper should be wellcited. If documents are not cited properly or not cited at all, then usingthese approaches will not be helpful. To overcome the problem, this paperdiscusses the approach Virtual Citation Proximity (VCP) which uses SiameseNeural Network along with the notion of citation proximity analysis andcontent-based filtering. To train this model, the actual distance between thetwo citations in a document is used as ground truth, this distance is the wordcount between the two citations. VCP is trained on Wikipedia articles for whichthe actual word count is available which is used to calculate the similaritybetween the documents. This can be used to calculate relatedness between twodocuments in a way they would have been cited in the proximity even if thedocuments are uncited. This approach has shown a great improvement inpredicting proximity with basic neural networks over the approach which usesthe Average Citation Proximity index value as the ground truth. This can beimproved by using a complex neural network and proper hyper tuning ofparameters.

Virtual Proximity Citation (VCP): A Supervised Deep Learning Method to Relate Uncited Papers On Grounds of Citation Proximity