Recommendation system based on semantic scholar mining and topic modeling on conference publications

• Published in: Soft computing (Berlin, Germany) Vol. 25; no. 5; pp. 3675 - 3696
• Main Authors: Jelodar, Hamed, Wang, Yongli, Xiao, Gang, Rabbani, Mahdi, Zhao, Ruxin, Ayobi, Seyedvalyallah, Hu, Peng, Masood, Isma
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Engineering; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Robotics; Topic modeling; Recommendation systems; Semantic mining; Natural language processing; Scholar article analysis
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-020-05397-3

Recommendation systems are of great assistance to online in computer science in various aspects of the Internet portals such as social networks and library websites. There are several approaches to implement recommendation systems. Latent Dirichlet allocation (LDA) is one of the popular techniques in topic modeling. Recently, researchers have proposed many approaches based on recommendation systems and LDA. Regarding the importance of the subject, in this paper, we discover the trends of the topics and find a relationship between LDA topics and Scholar-Context-documents. We apply probabilistic topic modeling based on Gibbs sampling algorithms for semantic mining from eight conference publications in computer science from the DBLP dataset. Based on the obtained experimental results, our semantic framework can be effective to help organizations to better organize these conferences and cover future research topics.