Application of educational resource recommendation techniques on a university campus

Authors

DOI:

https://doi.org/10.5902/2179460X75195

Keywords:

Recommendation Systems, Smart Campus, Collaborative Filtering, Content-based Filtering, Hybrid System

Abstract

The development of the Internet over the years has brought with it a massive increase in the amount of data present on the network, making the search for specific items a slow and complex task. Thus, tools have emerged with the goal of filtering information within websites and platforms, leaving aside everything that is irrelevant and providing the user with only what is likely to interest them. These tools are called recommendation systems. In addition, the field of education has also been affected by the problem of data overload, especially in recent years with the popularization of online education, where students demand new methods of research and learning beyond the classroom. Therefore, the objective of this article is to develop a personalized recommendation system for educational resources that, based on users' interests, makes predictions and generates lists of items that match their interests. It is also hoped that this platform can help in the integration and development of intelligent university campuses.

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Author Biographies

Vinícius Maran, Universidade Federal de Santa Maria

Adjunct Professor at the Federal University of Santa Maria (UFSM), Cachoeira do Sul campus. Permanent professor at the Graduate Program in Networked Educational Technologies (PPGTER-UFSM).

Martin Hideki Mensch Maruyama, Universidade Federal de Santa Maria

Graduated in Mechanical Engineering

Luan Willig Silveira, Universidade Federal de Santa Maria

Graduated in Electrical Engineering

References

Abouabdellah, A. Benfares, C., & elIdrissi, Y. E. B. (2017). Recommendation semantic of services in smart city. ACM International Conference Proceeding Series, Part F129474. DOI: https://doi.org/10.1145/3090354.3090407

Abualnaaj, K., Ahmed, V., & Saboor, S. (n.d.). A Strategic Framework for Smart Campus. Proceedings of the International Conference on Industrial Engineering and Operations Management Dubai, UAE, March 10-12, 2020

Chun-Mei, L., Jie-Teng, J., Shuo, D., Wei, P., Yan, Q., & Yi-Han, M. (2021). Personalized Recommendation Algorithm for books and its implementation. Journal of Physics: Conference Series, 1738(1). DOI: https://doi.org/10.1088/1742-6596/1738/1/012053

Dennouni, N., Lancieri, L., Peter, Y., & Slama, Z. (2018). Towards an incremental recommendation of POIs for mobile tourists without profiles. International Journal of Intelligent Systems and Applications, 10(10), 42–52. DOI: https://doi.org/10.5815/ijisa.2018.10.05

Eliyas, S., & Ranjana, P. (2022). Recommendation Systems: Content-Based Filtering vs Collaborative Filtering. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, 1360–1365. DOI: https://doi.org/10.1109/ICACITE53722.2022.9823730

Imbar, R. V., Langi, A. Z. R., & Supangkat, S. H. (2020, November 19). Smart Campus Model: A Literature Review. 7th International Conference on ICT for Smart Society: AIoT for Smart Society, ICISS 2020 - Proceeding. DOI: https://doi.org/10.1109/ICISS50791.2020.9307570

Jordán, J., Botti, V., Turró, C., & Valero, S. (2021). Using a hybrid recommending system for learning videos in flipped classrooms and moocs. Electronics (Switzerland), 10(11). DOI: https://doi.org/10.3390/electronics10111226

Karlgren, J. (1990). An Algebra for Recommendations An Algebra for Recommendations Using Reader Data as a Basis for Measuring Document Proximity.

Khan, N. Z. A., & Mahalakshmi, R. (2020). A novel user review-based contextual recommender system. International Journal of Modeling, Simulation, and Scientific Computing. DOI: https://doi.org/10.1142/S1793962323410027

Meng, H., & Cheng, Y. (2021). Research on Key Technologies of Intelligent Recommendation Based Online Education Platform in Big Data Environment. ACM International Conference Proceeding Series, 638–645. DOI: https://doi.org/10.1145/3473714.3473825

Mrhar, K., & Abik, M. (2019). Toward a deep recommender system for MOOCs platforms. ACM International Conference Proceeding Series, 173–177. DOI: https://doi.org/10.1145/3369114.3369157

Thannimalai, V., & Zhang, L. (2021). A CONTENT BASED AND COLLABORATIVE FILTERING RECOMMENDER SYSTEM. Proceedings - International Conference on Machine Learning and Cybernetics, 2021-December. DOI: https://doi.org/10.1109/ICMLC54886.2021.9737238

Xu, X., Wang, Y., & Yu, S. (2018). Teaching performance evaluation in smart campus. IEEE Access, 6, 77754–77766. DOI: https://doi.org/10.1109/ACCESS.2018.2884022

Zhang, Y., Dong, Z. Y., Lu, E., & Yip, C. (2022). A Systematic Review on Technologies and Applications in Smart Campus: A Human-Centered Case Study. In IEEE Access (Vol. 10, pp. 16134–16149). DOI: https://doi.org/10.1109/ACCESS.2022.3148735

Zheng, K., Wang, Y., Wu, Y., Yang, X., & Zheng, X. (2020). Collaborative filtering recommendation algorithm based on variational inference. International Journal of Crowd Science, 4(1), 31–44. DOI: https://doi.org/10.1108/IJCS-10-2019-0030

Published

2023-10-11

How to Cite

Maran, V., Maruyama, M. H. M., & Silveira, L. W. (2023). Application of educational resource recommendation techniques on a university campus. Ciência E Natura, 45, e17. https://doi.org/10.5902/2179460X75195

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