Using an Exponential Random Graph Model to Recommend Academic Collaborators
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 prospec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-06-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/10/6/220 |
| _version_ | 1819049933691420672 |
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| author | Hailah Al-Ballaa Hmood Al-Dossari Azeddine Chikh |
| author_facet | Hailah Al-Ballaa Hmood Al-Dossari Azeddine Chikh |
| author_sort | Hailah Al-Ballaa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T11:40:02Z |
| format | Article |
| id | doaj.art-38b19992b5704ed9ad53276afb41759d |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-21T11:40:02Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-38b19992b5704ed9ad53276afb41759d2022-12-21T19:05:20ZengMDPI AGInformation2078-24892019-06-0110622010.3390/info10060220info10060220Using an Exponential Random Graph Model to Recommend Academic CollaboratorsHailah Al-Ballaa0Hmood Al-Dossari1Azeddine Chikh2Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaInformation Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaComputer Sciences Department, College of Sciences, Abou Bekr Belkaid University, Tlemcen 13000, AlgeriaAcademic 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.https://www.mdpi.com/2078-2489/10/6/220academic collaborationrecommender systemcontext awarecollaborator recommender systemexponential random graph model |
| spellingShingle | Hailah Al-Ballaa Hmood Al-Dossari Azeddine Chikh Using an Exponential Random Graph Model to Recommend Academic Collaborators Information academic collaboration recommender system context aware collaborator recommender system exponential random graph model |
| title | Using an Exponential Random Graph Model to Recommend Academic Collaborators |
| title_full | Using an Exponential Random Graph Model to Recommend Academic Collaborators |
| title_fullStr | Using an Exponential Random Graph Model to Recommend Academic Collaborators |
| title_full_unstemmed | Using an Exponential Random Graph Model to Recommend Academic Collaborators |
| title_short | Using an Exponential Random Graph Model to Recommend Academic Collaborators |
| title_sort | using an exponential random graph model to recommend academic collaborators |
| topic | academic collaboration recommender system context aware collaborator recommender system exponential random graph model |
| url | https://www.mdpi.com/2078-2489/10/6/220 |
| work_keys_str_mv | AT hailahalballaa usinganexponentialrandomgraphmodeltorecommendacademiccollaborators AT hmoodaldossari usinganexponentialrandomgraphmodeltorecommendacademiccollaborators AT azeddinechikh usinganexponentialrandomgraphmodeltorecommendacademiccollaborators |