Recommendations of Research Articles by Experts: Visualizing Relationships and Expertise

The paper applied data analytics and network visualization to show the potentials of employing Faculty Opinions beyond literature recommendations by domain experts. Based on a set of highly recommended articles by at least four experts with a sum of 10 or more stars (A recommended article is assigned a score between one to three stars by the recommender.), this study tests the new ideas and methods of identifying and visualizing relationships between scientific papers, experts, and categories. Despite of the available dataset in the study is small, the findings show that a platform designed for recommending and retrieving publications has the potential as a knowledge base for seeking experts. The results are indicative rather than conclusive; further study should apply AI methodology to include multiple data sources to corroborate findings and to enhance the applicability of data visualization towards knowledge graphs

The manuscript was peer reviewed and accepted as a short paper for the proceedings and is presented as a poster.