Automatic recommendation system based on hybrid filtering algorithm-Reference-Cited by-同舟云学术

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Automatic recommendation system based on hybrid filtering algorithm

Published:2021-07-23 Issue: Volume: Page:
ISSN:1360-2357
Container-title:Education and Information Technologies
language:en
Short-container-title:Educ Inf Technol

Author:

Sharma Sunny^ORCID,Rana Vijay,Malhotra Manisha

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Education

Link

https://link.springer.com/content/pdf/10.1007/s10639-021-10643-8.pdf

Reference27 articles.

1. Adeniyi, D. A., Wei, Z., & Yongquanu, Y. (2016). Automated web usage data mining and recomme.ndation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90–108. https://doi.org/10.1016/j.aci.2014.10.001.

2. Al-Hassan, M., Lu, H., & Lu, J. (2015). A semantic enhanced hybrid recommendation approach: a case study of e-Government tourism service recommendation system. Decision Support Systems, 72, 97–109. https://doi.org/10.1016/j.dss.2015.02.001.

3. Ali, Z., Khusro, S., & Ullah, I. (2016). A hybrid book recommender system based on table of contents (toc) and association rule mining. In Proceedings of the 10th International Conference on Informatics and Systems (pp. 68–74). ACM. https://doi.org/10.1145/2908446.2908481.

4. De Gemmis, M., Lops, P., Musto, C., Narducci, F., & Semeraro, G. (2015). Semantics-aware content-based recommender systems. In Recommender Systems Handbook (pp. 119–159). Springer. https://doi.org/10.1007/978-1-4899-7637-6_4.

5. Elkahky, A. M., Song, Y., & He, X. (2015). A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web (pp. 278–288). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/2736277.2741667.

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