Recommender System for Scholarly Articles to Monitor COVID-19 Trends in Social Media Based on Low-Cost Topic Modeling
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27409-1_22
Reference25 articles.
1. Ahmed, W., Vidal-Alaball, J., Downing, J., López Seguí, F.: Covid-19 and the 5g conspiracy theory: social network analysis of twitter data. J. Med. Internet Res. 22(5), e19458 (2020)
2. Amami, M., Faiz, R., Stella, F., Pasi, G.: A graph based approach to scientific paper recommendation. In: Proceedings of the International Conference on Web Intelligence, pp. 777–782. WI ’17, Association for Computing Machinery, New York, NY, USA (2017)
3. Amami, M., Pasi, G., Stella, F., Faiz, R.: An LDA-based approach to scientific paper recommendation. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds.) Natural Language Processing and Information Systems, pp. 200–210. Springer International Publishing, Cham (2016)
4. Amara, A., Hadj Taieb, M.A., Ben Aouicha, M.: Multilingual topic modeling for tracking covid-19 trends based on facebook data analysis. Appl. Intell. 51(5), 3052–3073 (2021)
5. Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2015)
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