Skip to main content

2025 | OriginalPaper | Buchkapitel

An Efficient Framework for Student’s Club Recommender System Using Machine Learning Models

verfasst von : Jayanth Gurajada, R. Anu Keerthi, G. Santhandeep, S. Sandosh

Erschienen in: Innovative Computing and Communications

Verlag: Springer Nature Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In academic settings, scholars routinely encounter challenges in finding appropriate exercises for coeducational learning. The courses specialize in the Club Recommendation System (CRS) which aims to provide group counseling tailored to individual preferences using the Factorization Machines (FM) model. These are the extraordinary student attractions and loads of pictures to look at, or the dynamic mini club environments that act as serious issues that one must experience to strive for a premiership. However, it is a very difficult project to think through the complexity of preferences and membership issues; fortunately, the device is very rich with system learning techniques and factorization techniques that solve one's desire problems in models. The gift CRS after going through the facts processing with specific mission and matrix factorization will automate its best later to individualize group recommendations for each student. Through this approach, there will be a large community of students with diverse students interacting with many groups everywhere.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.CrossRef Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.CrossRef
2.
Zurück zum Zitat da Silva, F. L. (2023). A systematic literature review on educational recommender systems. da Silva, F. L. (2023). A systematic literature review on educational recommender systems.
3.
Zurück zum Zitat Algarni, S. (2023). Systematic review of recommendation systems for course selection. Algarni, S. (2023). Systematic review of recommendation systems for course selection.
4.
Zurück zum Zitat Roy, D. (2022). A systematic review and research perspective on recommender systems. Roy, D. (2022). A systematic review and research perspective on recommender systems.
5.
Zurück zum Zitat Lynn, N. D. (2021). A review on recommender systems for course selection. Lynn, N. D. (2021). A review on recommender systems for course selection.
6.
Zurück zum Zitat Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.CrossRef Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.CrossRef
7.
Zurück zum Zitat Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370.CrossRef Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370.CrossRef
8.
Zurück zum Zitat Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (230–237). Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (230–237).
9.
Zurück zum Zitat Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.CrossRef Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.CrossRef
10.
Zurück zum Zitat Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1–35). Springer. Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1–35). Springer.
11.
Zurück zum Zitat Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.CrossRef Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.CrossRef
12.
Zurück zum Zitat Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1–38.CrossRef Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1–38.CrossRef
13.
Zurück zum Zitat Said, A., & Bellogín, A. (2014). Comparative recommender system evaluation: Benchmarking recommendation frameworks. IEEE Transactions on Emerging Topics in Computing, 2(3), 329–342. Said, A., & Bellogín, A. (2014). Comparative recommender system evaluation: Benchmarking recommendation frameworks. IEEE Transactions on Emerging Topics in Computing, 2(3), 329–342.
14.
Zurück zum Zitat Kumar, A. (2020). Personalized university club recommendations using collaborative filtering. Kumar, A. (2020). Personalized university club recommendations using collaborative filtering.
15.
Zurück zum Zitat Smith, E. (2019). Enhancing student engagement through club recommendations in higher education. Smith, E. (2019). Enhancing student engagement through club recommendations in higher education.
16.
Zurück zum Zitat Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2016). Research-paper recommender systems: A literature survey. International Journal on Digital Libraries, 17(4), 305–338.CrossRef Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2016). Research-paper recommender systems: A literature survey. International Journal on Digital Libraries, 17(4), 305–338.CrossRef
17.
Zurück zum Zitat Johnson, B. (2018). A survey of recommender systems in higher education. Johnson, B. (2018). A survey of recommender systems in higher education.
18.
Zurück zum Zitat Brown, G. (2017). Personalized student club recommendations: Challenges and solutions. Brown, G. (2017). Personalized student club recommendations: Challenges and solutions.
19.
Zurück zum Zitat Gupta, R. (2015). Utilizing machine learning for personalized recommendations in higher education. Gupta, R. (2015). Utilizing machine learning for personalized recommendations in higher education.
20.
Zurück zum Zitat Patel, M. (2016). Dynamic recommender systems for university clubs. Patel, M. (2016). Dynamic recommender systems for university clubs.
Metadaten
Titel
An Efficient Framework for Student’s Club Recommender System Using Machine Learning Models
verfasst von
Jayanth Gurajada
R. Anu Keerthi
G. Santhandeep
S. Sandosh
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-4152-6_30