Abstract
Academic 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.
Highlights
Scientific collaboration is one of the defining features of modern science [1]
The recommendation system (RS) is evaluated with data derived from Scopus regarding collaborations of Computer and Information Sciences (CCIS) faculty in
Members who had at least a degree value equal to three were included
Summary
Scientific collaboration is one of the defining features of modern science [1]. The quality of higher education has been linked to effective collaborations [2]. Many collaborator RSs have been developed in recent years, but most are based on traditional approaches, such as the content-based approach, and employ fairly simple user models. Social network analysis (SNA) can be used to determine the social context of nodes (individuals) in the network. Different centrality measures are used to measure different influence and power attributes of nodes in the network. Some of these well-known measures are as follows: degree, which allows us to find nodes that exchange with numerous others and make their views noticeable; betweenness, which allows us to find nodes critical to collaborations across communities and information flow in the network; and eigenvectors, which allows us to find nodes that are not necessarily important, but that are connected to other important nodes.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
