A Recommender System for Scientific Resources Based on Recurrent Neural Networks
الموضوعات :
Hadis Ahmadian
1
(Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran)
Seyed Javad Mahdavi Chabok
2
(Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran)
Maryam Kheirabadi
3
(Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran)
الکلمات المفتاحية: Deep Learning Networks, Recurrent Methods, Educational Resource, Recommender System.,
ملخص المقالة :
Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses directly affect the number of active participants; on the other hand, it becomes difficult for teachers to guide students in choosing the appropriate learning resource due to the abundance of online learning resources. Students also find it challenging to decide which educational resources to choose according to their condition. The resource recommender system can be used as a Guide tool for educational resource recommendations to students so that these suggestions are tailored to the preferences and needs of each student. In this paper, it was presented a resource recommender system with the help of Bi-LSTM networks. Utilizing this type of structure involves both long-term and short-term interests of the user and, due to the gradual learning property of the system, supports the learners' behavioral changes. It has more appropriate recommendations with a mean accuracy of 0.95 and a loss of 0.19 compared to a similar article.
[1] Konstan, J. A., |Introduction to recommender systems. In Proceedings of the 2008 ACM SIGMOD international Conference on Management of Data, Vancouver, Canada, (Jun, 2008). [2] Resnick, P. and Varian, H. R. 1997. Recommender systems, Commun. ACM 40, 3 (Mar, 1997).
[3] Schafer, J. B., Konstan, J., and Riedi, J. Recommender systems in e-commerce. In Proceedings of the 1st ACM Conference on Electronic Commerce, Denver, Colorado, United States, (Nov, 1999).
[4] Gordan Durovic, Martina Holenko Dlab and Natasa Hoic-Bozic, Educational Recommender Systems: An Overview and Guidelines for Further Research and Development, Croatian Journal of Education Vol.20; No.2, 2018, 531-560.
[5] Paula Rodríguez et all, An educational recommender system based on argumentation theory, AI Communications 30 (2017), 19–36.
[6] learning Technology Standards Committee, IEEE Standard for Learning Object Metadata. Institute of Electrical and Electronics Engineers, New York (2002).
[7] Zhang, S.; Yao, L.; Sun, A. Deep learning based recommender system: A survey and new perspectives. arXiv 2017, arXiv:1707.07435.
[8] Ludewig,M.; Jannach, D. Evaluation of Session-based Recommendation Algorithms. arXiv 2018, arXiv:1803.09587.
[9] Hidasi, B.; Karatzoglou, A.; Baltrunas, L.; Tikk, D. Session-based Recommendations with Recurrent Neural Networks. In Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico, 2-4 May 2016; pp. 1-10.
[10] Paula Rodriguez et all, An educational recommender system based on argumentation theory, AI Communications 30 (2017), 19-36
. [11] Zhang, H., et al., MOOCRC: A highly accurate resource recommendation model for use in MOOC environments. Mobile Networks and Applications, 2019. 24(1): p. 34-46.
[12] Shuai Zhang, et. all, \Deep Learning based Recommender System: A Survey and New Perspectives", ACM Computing Surveys, Vol. 1, No. 1,2018.
[13] Zeynep Batmaz, Ali Yurekli, Alper Bilge and Cihan Kaleli, A review on deep learning for recommender systems: challenges and remedies, Springer Nature B.V. 2018.
[14] SHUAI ZHANG,LINA YAO, AIXIN SUN and YI TAY, Nanyang Technological University Deep Learning based Recommender System: A Survey and New Perspectives, 2018, ACM Computing Surveys, Vol. 1, No. 1, Article 1.
[15] Chen, H., et al., Enhanced learning resource recommendation based on online learning style model. Tsinghua Science and Technology, 2019. 25(3): p. 348-356.
[16] Li, R., et al., Online learning style modeling for course recommendation, in Recent Developments in Intelligent Computing, Communication and Devices. 2019, Springer. p. 1035-1042.
[17] Hagemann, N., M.P. O'Mahony, and B. Smyth. Visualising module dependencies in academic recommendations. in Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. 2019.
[18] Rodríguez, P., et al., An educational recommender system based on argumentation theory. AI Communications, 2017. 30(1): p. 19-36.
[19] Dawen Liang, Rahul G Krishnan, Mathew D Ho man, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. arXiv preprint arXiv:1802.05814 (2018).
[20] Neto, J. Multi-agent web recommender system for online educational environments. in International Conference on Practical Applications of Agents and Multi-Agent Systems. 2017. Springer.
[21] Rodríguez, P., N. Duque, and S. Rodríguez, Integral Multi-agent Model Recommendation of Learning Objects, for Students and Teachers, in Management Intelligent Systems. 2013, Springer. p. 127-134.
[22] Ahmadian Yazdi, H., S.J. Seyyed Mahdavi Chabok, and M. Kheirabadi, Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence, 2021: p. 1-24.
[23] Shanker, M., M.Y. Hu, and M.S. Hung, Effect of data standardization on neural network training. Omega, 1996. 24(4): p. 385-397. [24] Zhang, H., Huang, T., Zhihan, Lv., Liu, S., and Yang, H. MOOCRC: A Highly Accurate Resource Recommen- dation Model for Use in MOOC Environments. Springer, Mobile Networks and Applications, Springer ,2018.
[25]Charnelli, M.E., Sistemas recomendadores aplicados en Educación. 2019, Universidad Nacional de La Plata.
[26]Li, R., et al., Online learning style modeling for course recommendation, in Recent Developments in Intelligent Computing, Communication and Devices. 2019, Springer. p. 1035-1042.
[27]Yan, L., et al. Learning Resource Recommendation in E-Learning Systems Based on Online Learning Style. in International Conference on Knowledge Science, Engineering and Management. 2021. Springer. [28] P. Resnick and H.R. Varian. Recommender systems. Communications of the ACM, 40(3):56.58, (1997).
