Using an Exponential Random Graph Model to Recommend Academic Collaborators
AbstractAcademic collaboration networks can be formed by grouping different faculty members into a single group. Grouping these faculty members together is a complex process that involves searching multiple web pages in order to collect and analyze information, and establishing new connections among prospective collaborators. A recommender system (RS) for academic collaborations can help reduce the time and effort required to establish a new collaboration. Content-based recommendation system make recommendations based on similarity without taking social context into consideration. Hybrid recommender systems can be used to combine similarity and social context. In this paper, we propose a weighting method that can be used to combine two or more social context factors in a recommendation engine that leverages an exponential random graph model (ERGM) based on historical network data. We demonstrate our approach using real data from collaborations with faculty members at the College of Computer and Information Sciences (CCIS) in Saudi Arabia. Our results demonstrate that weighting social context factors helps increase recommendation accuracy for new users. View Full-Text
Share & Cite This Article
Al-Ballaa, H.; Al-Dossari, H.; Chikh, A. Using an Exponential Random Graph Model to Recommend Academic Collaborators. Information 2019, 10, 220.
Al-Ballaa H, Al-Dossari H, Chikh A. Using an Exponential Random Graph Model to Recommend Academic Collaborators. Information. 2019; 10(6):220.Chicago/Turabian Style
Al-Ballaa, Hailah; Al-Dossari, Hmood; Chikh, Azeddine. 2019. "Using an Exponential Random Graph Model to Recommend Academic Collaborators." Information 10, no. 6: 220.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.