Research paper recommendation system based on multiple features from citation network

With tremendous growth in the volume of published scholarly work, it becomes quite diffcult for researchers to fnd appropriate documents relevant to their research topic. Many
research paper recommendation approaches have been proposed and implemented which
include collaborative fltering, content-based, metadata, link-based and multi-level citation network. In this research, a novel Research paper Recommendation system is proposed by integrating Multiple Features (RRMF). RRMF constructs a multi-level citation
network and collaboration network of authors for feature integration. The structure and
semantic based relationships are identifed from the citation network whereas key authors
are extracted from collaboration network for the study. For experimentation and analysis,
AMiner v12 DBLP-Citation Network is used that covers 4,894,081 academic papers and
45,564,149 citation relationships. The information retrieval metrices including Mean Average Precision, Mean Reciprocal Rank and Normalized Discounted Cumulative Gain are
used for evaluating the performance of proposed system. The research results of proposed
approach RRMF are compared with baseline Multilevel Simultaneous Citation Network
(MSCN) and Google Scholar. Consequently, comparison of RRMF showed 87% better recommendations than the traditional MSCN and Google Scholar