Main Reference PaperExploiting Fine-grained Co-authorship for Personalized Citation Recommendation, IEEE Access, June 2017 [python].
  • Most richest information assisted Graph based model for citation recommendation system is proposed that focuses the non binary Co-authorship fine-grained network model structure, author-paper, paper-citation and paper-keyword relations.

Description
  • Most richest information assisted Graph based model for citation recommendation system is proposed that focuses the non binary Co-authorship fine-grained network model structure, author-paper, paper-citation and paper-keyword relations.

  • To generate personalized query oriented recommendation

  • To reduce the information loss using fine-grained model.

Aim & Objectives
  • To generate personalized query oriented recommendation

  • To reduce the information loss using fine-grained model.

  • Publication data and author location information are incorporated.

Contribution
  • Publication data and author location information are incorporated.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-Depending on the complexity of the project and requirements.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.