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Hybrid Course Recommendation System Design for a Real-Time Student Automation Application

Year 2021, Issue: 26 - Ejosat Special Issue 2021 (HORA), 85 - 90, 31.07.2021
https://doi.org/10.31590/ejosat.944596

Abstract

Recommender systems provide personalized suggestions by processing user and item information and interactions. Personalized product recommendations make it easier for users to access products that interest them. Course recommendation systems, on the other hand, aim to guide students to fields of interest in which they can succeed. On e-learning sites, there are many courses and students from different fields. Also, students can select courses from other than the fields they are studying. However, students in educational institutions must follow a curriculum. Since each educational institution has distinct constraints on course selection, a specific approach to the problem is required to develop a course recommender system. Due to the restrictive nature of the problem, developing a recommendation system for institutions is considered challenging. Therefore, students consult a faculty member when selecting a course for enrollment. In this study, a hybrid recommender system is proposed using student and course information with collaborative filtering and content-based filtering models. The proposed system provides consistent recommendations by using explicit and implicit data, without predefined association rules. The collaborative filtering algorithms use grades as rating values. The content-based filtering algorithms utilize text-based information about students and courses by converting them into feature vectors using natural language processing methods. In the combination phase of the hybrid recommender system, only one of the collaborative filtering and one of the content-based filtering models are used with different ensembling methods. It is found that the suggested hybrid recommender system can achieve outperforming results for all evaluation metrics. The results show the values of the rank-aware metrics Precision@N, AP@N, mAP@N, and NDCG@N for the individual models and the hybrid models with different combinations. In particular, for content-based filtering with Bayesian personalized ranking, the hybrid model performs better than any algorithm in practice.

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References

  • Bagga, A., & Baldwin, B. (1998). Entity-Based Cross-Document Core f erencing Using the Vector Space Model. Paper presented at the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1.
  • Bell, R. M., & Koren, Y. (2007). Lessons from the netflix prize challenge. Acm Sigkdd Explorations Newsletter, 9(2), 75-79.
  • Bhumichitr, K., Channarukul, S., Saejiem, N., Jiamthapthaksin, R., & Nongpong, K. (2017, 12-14 July 2017). Recommender Systems for university elective course recommendation. Paper presented at the 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE).
  • Booker, Q. E. (2009). A student program recommendation system prototype. Issues in Information Systems, 544-551.
  • Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4), 331-370.
  • Busa-Fekete, R., Szarvas, G., Elteto, T., & Kégl, B. (2012). An apple-to-apple comparison of learning-to-rank algorithms in terms of normalized discounted cumulative gain. Paper presented at the ECAI 2012-20th European Conference on Artificial Intelligence: Preference Learning: Problems and Applications in AI Workshop.
  • Bydžovská, H. (2016). Course Enrollment Recommender System. International Educational Data Mining Society.
  • Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487-1524.
  • Carmona, C., Castillo, G., & Millán, E. (2007). Discovering student preferences in e-learning. Paper presented at the Proceedings of the international workshop on applying data mining in e-learning.
  • Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004). Ensemble selection from libraries of models. Paper presented at the Proceedings of the twenty-first international conference on Machine learning.
  • Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. In Evolution of teaching and learning paradigms in intelligent environment (pp. 183-221): Springer.
  • Grechanik, M., Fu, C., Xie, Q., McMillan, C., Poshyvanyk, D., & Cumby, C. (2010). A search engine for finding highly relevant applications. Paper presented at the Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1.
  • Guo, G. (2012). Resolving data sparsity and cold start in recommender systems. Paper presented at the International Conference on User Modeling, Adaptation, and Personalization.
  • Hernando, A., Bobadilla, J., Ortega, F., & Gutiérrez, A. (2017). A probabilistic model for recommending to new cold-start non-registered users. Information Sciences, 376, 216-232.
  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
  • Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12-32.
  • McFee, B., & Lanckriet, G. R. (2010). Metric learning to rank. Paper presented at the ICML.
  • Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. Paper presented at the Proceedings of the first instructional conference on machine learning.
  • Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE Access, 6, 14277-14284.
  • Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618.
  • Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data mining and knowledge discovery, 5(1-2), 115-153.
  • Sewell, M. (2008). Ensemble learning. RN, 11(02), 1-34.
  • Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. Paper presented at the Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval.

Gerçek Zamanlı Öğrenci Otomasyon Uygulaması için Hibrit Ders Öneri Sistemi Tasarımı

Year 2021, Issue: 26 - Ejosat Special Issue 2021 (HORA), 85 - 90, 31.07.2021
https://doi.org/10.31590/ejosat.944596

Abstract

Öneri sistemleri, kişi ve öğe bilgilerini kullanarak ve birbirleriyle olan etkileşimlerini işleyerek kullanıcılara göre özelleştirilmiş öneriler sunmaktadır. Kişiselleştirilmiş ürün önerileri, kullanıcıların ilgilerini çeken ürünlere erişmelerini kolaylaştırmaktadır. Ders öneri sistemleri ise öğrencileri ilgilendikleri ve başarılı olabilecekleri alanlara yönlendirmeyi amaçlamaktadır. E-öğrenme sitelerinde farklı disiplinlerden çok sayıda kurs ve öğrenci bulunmaktadır. Bu durumun yanı sıra, öğrenciler eğitim aldıkları disiplinler dışındaki diğer alanlardan ders alabilmektedir. Buna karşın, eğitim kurumlarındaki öğrenciler ise önceden belirlenmiş bir müfredatı takip etmek zorundadır. Her eğitim kurumu, ders seçimi için farklı kısıtlara sahip olduğundan, ders öneri sistemi geliştirme problemine özel bir yaklaşım gerekmektedir. Problemin sınırlayıcı doğası gereği, eğitim kurumları için ders öneri sistemi geliştirilmesi zorlu bir alan olarak kabul edilmektedir. Bu nedenle, öğrenciler kayıt için ders seçerken bir öğretim üyesine danışmaktadırlar. Bu çalışmada, öğrenci ve ders bilgileri ile işbirlikçi filtreleme ve içerik tabanlı filtreleme modelleri kullanan hibrit öneri sistemi önerilmiştir. Sistem, önceden tanımlanmış ilişkilendirme kuralları olmadan, belirgin ve dolaylı verileri kullanarak tutarlı öneriler sunmaktadır. İşbirlikçi filtreleme algoritması, öğrencilerin notlarını değerlendirme skoru olarak kullanmaktadır. İçerik tabanlı filtreleme algoritması ise öğrenciler ve dersler hakkındaki metin formatında bulunan bilgileri, doğal dil işleme yöntemleri ile özellik vektörlerine dönüştürerek kullanmaktadır. Hibrit öneri sistemini oluşturma işleminde, işbirlikçi filtreleme ve içerik tabanlı filtreleme modellerinden birer tane seçilmiş ve farklı birleştirme yöntemleri uygulanmıştır. Deneysel sonuçlarda ise, sunulan hibrit öneri sisteminin kendisini oluşturan algoritmalardan, tüm değerlendirme metriklerinde, daha başarılı sonuçlar elde edebildiği görülmüştür. Sonuç bölümünde, farklı kombinasyonlar ile oluşturulmuş hibrit modeller için Precision@N, AP@N, mAP@N ve NDCG@N sıralamaya duyarlı metrik değerleri gösterilmektedir. Özellikle, içerik tabanlı filtreleme ve Bayes kişiselleştirilmiş sıralamasından oluşan hibrit model, diğer tüm tekil modellerden daha iyi performans göstermiştir.

Project Number

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References

  • Bagga, A., & Baldwin, B. (1998). Entity-Based Cross-Document Core f erencing Using the Vector Space Model. Paper presented at the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1.
  • Bell, R. M., & Koren, Y. (2007). Lessons from the netflix prize challenge. Acm Sigkdd Explorations Newsletter, 9(2), 75-79.
  • Bhumichitr, K., Channarukul, S., Saejiem, N., Jiamthapthaksin, R., & Nongpong, K. (2017, 12-14 July 2017). Recommender Systems for university elective course recommendation. Paper presented at the 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE).
  • Booker, Q. E. (2009). A student program recommendation system prototype. Issues in Information Systems, 544-551.
  • Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4), 331-370.
  • Busa-Fekete, R., Szarvas, G., Elteto, T., & Kégl, B. (2012). An apple-to-apple comparison of learning-to-rank algorithms in terms of normalized discounted cumulative gain. Paper presented at the ECAI 2012-20th European Conference on Artificial Intelligence: Preference Learning: Problems and Applications in AI Workshop.
  • Bydžovská, H. (2016). Course Enrollment Recommender System. International Educational Data Mining Society.
  • Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487-1524.
  • Carmona, C., Castillo, G., & Millán, E. (2007). Discovering student preferences in e-learning. Paper presented at the Proceedings of the international workshop on applying data mining in e-learning.
  • Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004). Ensemble selection from libraries of models. Paper presented at the Proceedings of the twenty-first international conference on Machine learning.
  • Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. In Evolution of teaching and learning paradigms in intelligent environment (pp. 183-221): Springer.
  • Grechanik, M., Fu, C., Xie, Q., McMillan, C., Poshyvanyk, D., & Cumby, C. (2010). A search engine for finding highly relevant applications. Paper presented at the Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1.
  • Guo, G. (2012). Resolving data sparsity and cold start in recommender systems. Paper presented at the International Conference on User Modeling, Adaptation, and Personalization.
  • Hernando, A., Bobadilla, J., Ortega, F., & Gutiérrez, A. (2017). A probabilistic model for recommending to new cold-start non-registered users. Information Sciences, 376, 216-232.
  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
  • Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12-32.
  • McFee, B., & Lanckriet, G. R. (2010). Metric learning to rank. Paper presented at the ICML.
  • Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. Paper presented at the Proceedings of the first instructional conference on machine learning.
  • Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE Access, 6, 14277-14284.
  • Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618.
  • Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data mining and knowledge discovery, 5(1-2), 115-153.
  • Sewell, M. (2008). Ensemble learning. RN, 11(02), 1-34.
  • Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. Paper presented at the Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval.
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Alper Arık 0000-0002-6542-6286

Savaş Okyay 0000-0003-3955-6324

Nihat Adar 0000-0002-0555-0701

Project Number -
Publication Date July 31, 2021
Published in Issue Year 2021 Issue: 26 - Ejosat Special Issue 2021 (HORA)

Cite

APA Arık, A., Okyay, S., & Adar, N. (2021). Hybrid Course Recommendation System Design for a Real-Time Student Automation Application. Avrupa Bilim Ve Teknoloji Dergisi(26), 85-90. https://doi.org/10.31590/ejosat.944596