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Using an Exponential Random Graph Model to Recommend Academic Collaborators

1
Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
2
Computer Sciences Department, College of Sciences, Abou Bekr Belkaid University, Tlemcen 13000, Algeria
*
Author to whom correspondence should be addressed.
Information 2019, 10(6), 220; https://doi.org/10.3390/info10060220
Received: 9 April 2019 / Revised: 17 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
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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. View Full-Text
Keywords: academic collaboration; recommender system; context aware; collaborator recommender system; exponential random graph model academic collaboration; recommender system; context aware; collaborator recommender system; exponential random graph model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Al-Ballaa, H.; Al-Dossari, H.; Chikh, A. Using an Exponential Random Graph Model to Recommend Academic Collaborators. Information 2019, 10, 220.

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