Research Paper Recommender Systems on Big Scholarly Data

Abstract

Rapidly growing scholarly data has been coined Big Scholarly Data (BSD), which includes hundreds of millions of authors, papers, citations, and other scholarly information. The effective utilization of BSD may expedite various research-related activities, which include research management, collaborator discovery, expert finding and recommender systems. Research paper recommender systems using smaller datasets have been studied with inconclusive results in the past. To facilitate research to tackle the BSD challenge, we built an analytic platform and developed a research paper recommender system. The recommender system may help researchers find research papers closely matching their interests. The system is not only capable of recommending proper papers to individuals based on his/her profile, but also able to recommend papers for a research field using the aggregated profiles of researchers in the research field.