E-learning Recommender System based on k-Nearest Neighbor (KNN), Singular Value Decomposition (SVD), and CoClustering Approaches
How to cite (IJASEIT) :
Z. Wang, A. Maalla, and M. Liang, “Research on E-Commerce Personalized Recommendation System based on Big Data Technology,” in 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), IEEE, Dec. 2021, pp. 909–913. doi:10.1109/ICIBA52610.2021.9687955.
K. Sharma, D. Parashar, K. BasavaRaju, D. K. Singh, A. Kazi, and G. Kumar, “The Role of Big Data Analytics in Enhancing Customer Interactions and Loyalty,” in 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC), IEEE, Dec. 2023, pp. 308–311. doi: 10.1109/PEEIC59336.2023.10451643.
M. M. Hussain, S. Akbar, S. A. Hassan, M. W. Aziz, and F. Urooj, “Prediction of Student’s Academic Performance through Data Mining Approach,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 241–251, Feb. 2024, doi: 10.33093/jiwe.2024.3.1.16.
A. Dogan and D. Birant, “Machine learning and data mining in manufacturing,” Expert Syst Appl, vol. 166, p. 114060, Mar. 2021, doi:10.1016/j.eswa.2020.114060.
U. Vellappan, L. Liyen, and S. Y. Lim, “Engaging Learning Experience: Enhancing Productivity Software Lessons with Screencast Videos,” Journal of Informatics and Web Engineering, vol. 2, no. 2, pp. 189–200, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.14.
A. and S. A. and T. H. Aberbach Hicham and Jeghal, “E-learning Recommendation Systems: A Literature Review,” Lecture Notes in Networks and Systems, vol 454. Springer, 2022. doi: 10.1007/978-3-031-01942-5_36
C. Susaie, C.-K. Tan, and P.-Y. Goh, “Learning Experience with LearnwithEmma,” Journal of Informatics and Web Engineering, vol. 1, no. 2, 2022, doi: 10.33093/jiwe.2022.1.2.3.
Z. Yujiao, L. W. Ang, S. Shaomin, and S. Palaniappan, “Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM,” Journal of Informatics and Web Engineering, vol. 2, no. 2, 2023, doi: 10.33093/jiwe.2023.2.2.3.
S. S. Kundu, D. Sarkar, P. Jana, and D. K. Kole, “Personalization in Education Using Recommendation System: An Overview,” 2021, pp. 85–111. doi: 10.1007/978-981-15-8744-3_5.
Y. Hu, Y. Koren, and C. Volinsky, “Collaborative Filtering for Implicit Feedback Datasets,” in 2008 Eighth IEEE International Conference on Data Mining, IEEE, Dec. 2008, pp. 263–272. doi:10.1109/ICDM.2008.22.
E. Blasch et al., “Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges,” IEEE Aerospace and Electronic Systems Magazine, vol. 36, no. 7, pp. 80–93, 2021, doi:10.1109/MAES.2020.3049030.
N. Zamri, N. Palanichamy, and S.-C. Haw, “College Course Recommender System based on Sentiment Analysis,” Int J Adv Sci Eng Inf Technol, vol. 13, no. 5, p. 1984, Oct. 2023, doi:10.18517/ijaseit.13.5.19032.
I. Kumar, J. Rawat, N. Mohd, and S. Husain, “Opportunities of Artificial Intelligence and Machine Learning in the Food Industry,” J Food Qual, vol. 2021, pp. 1–10, Jul. 2021, doi:10.1155/2021/4535567.
S. Fuchs, C. Drieschner, and H. Wittges, “Improving Support Ticket Systems Using Machine Learning: A Literature Review,” 2022. doi:10.24251/HICSS.2022.238.
P. Rani, S. Kotwal, J. Manhas, V. Sharma, and S. Sharma, “Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments,” Archives of Computational Methods in Engineering, vol. 29, no. 3, pp. 1801–1837, May 2022, doi:10.1007/s11831-021-09639-x.
M. Lee and S. Oh, “An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks,” Applied Sciences, vol. 11, no. 6, p. 2530, Mar. 2021, doi:10.3390/app11062530.
M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowl Based Syst, vol. 226, p. 107134, 2021, doi:10.1016/j.knosys.2021.107134.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput Sci, vol. 2, no. 3, p. 160, May 2021, doi: 10.1007/s42979-021-00592-x.
Ms. T. Sharad Phalle and Prof. S. Bhushan, “Content Based Filtering And Collaborative Filtering: A Comparative Study,” Journal of Advanced Zoology, pp. 96–100, Mar. 2024, doi:10.53555/jaz.v45iS4.4158.
P. Bahrani, B. Minaei-Bidgoli, H. Parvin, M. Mirzarezaee, and A. Keshavarz, “A hybrid semantic recommender system enriched with an imputation method,” Multimed Tools Appl, vol. 83, no. 6, pp. 15985–16018, 2024, doi: 10.1007/s11042-023-15258-4.
J. Latrech, Z. Kodia, and N. Ben Azzouna, “CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning,” J Supercomput, vol. 80, no. 1, pp. 1160–1182, Jan. 2024, doi: 10.1007/s11227-023-05519-2.
G. Behera and N. Nain, “Handling data sparsity via item metadata embedding into deep collaborative recommender system,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9953–9963, Nov. 2022, doi: 10.1016/j.jksuci.2021.12.021.
J. Wang, A. Kamran, F. Shahzad, and N. A. Syed, “Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendations,” Syst Res Behav Sci, Feb. 2024, doi: 10.1002/sres.3000.
L. Chen, R. Xiong, and Y. Ji, “Application of SVM model based on collaborative filtering hybrid algorithm in e-commerce recommendation,” International Journal of Computers and Applications, pp. 1–9, Feb. 2024, doi:10.1080/1206212X.2024.2309809.
E. Adeoye, H. K, R. E, and K. P, “Hybrid Recommendation Systems,” SSRN Electronic Journal, 2024, doi: 10.2139/ssrn.4712941.
S. Suriati, M. Dwiastuti and T. Tulus, “Weighted hybrid technique for recommender system”, Journal of Physics: Conference Series, vol. 930, 2017, doi: 10.1088/1742-6596/930/1/012050
J. K. Tarus, Z. Niu, and D. Kalui, “A hybrid recommender system for e-learning based on context awareness and sequential pattern mining,” Soft comput, vol. 22, no. 8, pp. 2449–2461, Apr. 2018, doi:10.1007/s00500-017-2720-6.
S. B. Aher and L. M. R. J. Lobo, “Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data,” Knowl Based Syst, vol. 51, pp. 1–14, Oct. 2013, doi: 10.1016/j.knosys.2013.04.015.
Huynh-Ly Thanh-Nhan, Huu-Hoa Nguyen, and N. Thai-Nghe, “Methods for building course recommendation systems,” in 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE), IEEE, Oct. 2016, pp. 163–168. doi:10.1109/KSE.2016.7758047.
R. Obeidat, R. Duwairi, and A. Al-Aiad, “A Collaborative Recommendation System for Online Courses Recommendations,” in 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), IEEE, Aug. 2019, pp. 49–54. doi: 10.1109/Deep-ML.2019.00018.
L. Zhao and Z. Pan, “Research on Online Course Recommendation Model Based on Improved Collaborative Filtering Algorithm,” in 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), IEEE, Apr. 2021, pp. 437–440. doi:10.1109/ICCCBDA51879.2021.9442575.
H. Aoulad Ali, C. Mohamed, B. Abdelhamid, and T. El Alami, “A Course Recommendation System for Moocs Based On Online Learning,” in 2021 XI International Conference on Virtual Campus (JICV), 2021, pp. 1–3. doi: 10.1109/JICV53222.2021.9600379.
H. Y. Chan, R. Rajamohan, K. H. Gan, and N.-H. Samsudin, “Text Analytics on Course Reviews from Coursera Platform,” in 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), IEEE, 2021, pp. 1–6. doi:10.1109/IICAIET51634.2021.9573868.
Z. Y. Poo, C. Y. Ting, Y. P. Loh, and K. I. Ghauth, “Multi-Label Classification with Deep Learning for Retail Recommendation,” Journal of Informatics and Web Engineering, vol. 2, no. 2, pp. 218–232, 2023, doi: 10.33093/jiwe.2023.2.2.16.
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