Abstract
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.
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Acknowledgements
This article has been awarded by the National Natural Science Foundation of China (61941113, 81674099, 61502233), the Fundamental Research Fund for the Central Universities (30918015103, 30918012204), Nanjing Science and Technology Development Plan Project (201805036), and “13th Five-Year” equipment field fund (61403120501), China Academy of Engineering Consulting Research Project(2019-ZD-1-02-02).
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Hamed Jelodar, Yongli Wang, Gang Xiao, Mahdi Rabbani, Ruxin Zhao, Seyedvalyallah Ayobi, Peng Hu, and Isma Masood declare no conflict of interest directly related to the submitted work.
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Jelodar, H., Wang, Y., Xiao, G. et al. Recommendation system based on semantic scholar mining and topic modeling on conference publications. Soft Comput 25, 3675–3696 (2021). https://doi.org/10.1007/s00500-020-05397-3
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DOI: https://doi.org/10.1007/s00500-020-05397-3