References
- 1. Karataev, E., V. Zadorozhny. Adaptive Social Learning Based on Crowdsourcing. – IEEE Transactions on Learning Technologies, Vol. 10, April 2017, No 2, pp. 128-139.10.1109/TLT.2016.2515097
- 2. KPMG & Google (2017). Online Education in India: 2021. Accessed 21 Janurary 2019. https://assets.kpmg.com/content/dam/kpmg/in/pdf/2017/05/Online-Education-in-India-2021.pdf/
- 3. Ricci, F., L. Rokach, B. Shapira. Recommender Systems: Introduction and Challenges. – Recommender Systems Handbook, Boston, MA, USA: Springer, 2015, pp. 1-34.10.1007/978-1-4899-7637-6
- 4. Wan, S., Z. Niu. A Hybrid e-Learning Recommendation Approach Based on Learners’ Influence Propagation. – IEEE Transactions on Knowledge and Data Engineering, January 2019.10.1109/TKDE.2019.2895033
- 5. Adomavicius, G., A. Tuzhilin. Context-Aware Recommender Systems. – Recommender Systems Handbook, Boston, MA, USA: Springer, 2011, pp. 217-253.10.1007/978-0-387-85820-3_7
- 6. Ren, L., W. Wang. An SVM-Based Collaborative Filtering Approach for Top-N Web Services Recommendation. – Future Generation Computer Systems, Vol. 78, January 2018, pp. 531-543.10.1016/j.future.2017.07.027
- 7. Aggarwal, C. Recommender System the Textbook. Switzerland, Springer International Publishing, 2016.
- 8. Paradarami, T. K., N. D. Bastian, J. L. Wightman. A Hybrid Recommender System Using Artificial Neural Networks. – Expert Systems with Applications, Vol. 83, October 2017, pp. 300-313.10.1016/j.eswa.2017.04.046
- 9. Fazeli, S., B. Loni, H. Drachsler, P. Sloep. Which Recommender System Can Best Fit Social Learning Platforms? – In: Proc. of European Conference on Technology Enhanced Learning, Springer, Cham, 2014, pp. 84-97.10.1007/978-3-319-11200-8_7
- 10. Koren, Y. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. – In: Proc. of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2008, pp. 426-434.
- 11. Hofmann, T. Latent Semantic Models for Collaborative Filtering. – ACM Transactions on Information Systems (TOIS), Vol. 22, January 2004, No 1, pp. 89-115.10.1145/963770.963774
- 12. Koren, Y., R. Bell, C. Volinsky. Matrix Factorization Techniques for Recommender Systems. – Computer, Vol. 42, August 2009, No 8, pp. 30-37.10.1109/MC.2009.263
- 13. Yu, H. F., C. J. Hsieh, S. Si, I. Dhillon. Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems. – In: IEEE 12th International Conference on Data Mining, IEEE, 2012, pp. 765-774.10.1109/ICDM.2012.168
- 14. Lops, P., M. De Gemmis, G. Semeraro. Content-Based Recommender Systems: State of the Art and Trends. – Recommender Systems Handbook, Boston, MA, USA, Springer, 2011, pp. 73-105.10.1007/978-0-387-85820-3_3
- 15. Konstan, J., M. Ekstrand. Introduction to Matrix Factorization and Dimensionality Reduction. – Matrix Factorization and Advanced Techniques, 2018. https://www.coursera.org/lecture/matrix-factorization/introduction-to-matrix-factorization-and-dimensionality-reduction-ncbvP
- 16. Jahrer, M., A. Töscher, R. Legenstein. Combining Predictions for Accurate Recommender Systems. – In: Proc. of 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010, pp. 693-702.10.1145/1835804.1835893
- 17. Fatahi, S., H. Moradi, L. Kashani-Vahid. A Survey of Personality and Learning Styles Models Applied in Virtual Environments with Emphasis on e-Learning Environments. – Artificial Intelligence Review, Vol. 46, October 2016, No 3, pp. 413-429.10.1007/s10462-016-9469-7
- 18. Rani, M., R. Nayak, O. P. Vyas. An Ontology-Based Adaptive Personalized e-Learning System, Assisted by Software Agents on Cloud Storage. – Knowledge-Based Systems, Vol. 90, December 2015, pp. 33-48.10.1016/j.knosys.2015.10.002
- 19. Truong, H. M. Integrating Learning Styles and Adaptive e-Learning System: Current Developments, Problems and Opportunities. – Computers in Human Behavior, Vol. 55, February 2016, pp. 1185-1193.10.1016/j.chb.2015.02.014
- 20. Wan, S., Z. Niu. An e-Learning Recommendation Approach Based on the Self-Organization of Learning Resource. – Knowledge-Based Systems, Vol. 160, November 2018, pp. 71-87.10.1016/j.knosys.2018.06.014
- 21. Soloman, B. A., N. Carolina, R. M. Felder. Index of learning Styles Questionnaire. – Learning, 1996, pp. 1-5.
- 22. Ouf, S., M. A. Ellatif, S. E. Salama, Y. Helmy. A Proposed Paradigm for Smart Learning Environment Based on Semantic Web. – Computers in Human Behavior, Vol. 72, July 2017, pp. 796-818.10.1016/j.chb.2016.08.030
- 23. Provitera, M. J., E. Esendal. Learning and Teaching Styles in Management Education: Identifying, Analyzing, and Facilitating. – Journal of College Teaching & Learning, Vol. 5, January 2008, No 1, pp. 69-78.10.19030/tlc.v5i1.1323
- 24. Bourkoukou, O., E. El Bachari, M. El Adnani. A Recommender Model in e-Learning Environment. – Arabian Journal for Science and Engineering, Vol. 42, February 2017, No 2, pp. 607-617.10.1007/s13369-016-2292-2
- 25. Tarus, J. K., Z. Niu, D. Kalui. A Hybrid Recommender System for e-Learning Based on Context Awareness and Sequential Pattern Mining. – Soft Computing, Vol. 22, April 2018, No 8, pp. 2449-2461.10.1007/s00500-017-2720-6
- 26. Gorakala, S. K., M. Usuelli. Data Mining Techniques Used in Recommender Systems. – Building a Recommendation System with R, Birmingham, UK, Packt Publishing, 2015, pp. 9-15.
- 27. Amatriain, X., A. Jaimes, N. Oliver, J. M. Pujol. Data Mining Methods for Recommender Systems. – Recommender Systems Handbook, Boston, MA, USA, Springer, 2011, pp. 39-71.10.1007/978-0-387-85820-3_2
- 28. Chen, J., H. Wang, Z. Yan. Evolutionary Heterogeneous Clustering for Rating Prediction Based on User Collaborative Filtering. – Swarm and Evolutionary Computation, Vol. 38, February 2018, pp. 35-41.10.1016/j.swevo.2017.05.008
- 29. Margaris, D., C. Vassilakis, P. Georgiadis. Query Personalization Using Social Network Information and Collaborative Filtering Techniques. – Future Generation Computer Systems, Vol. 78, January 2018, pp. 440-450.10.1016/j.future.2017.03.015
- 30. Xiao, J., M. Wang, B. Jiang, J. Li. A Personalized Recommendation System with Combinational Algorithm for Online Learning. – Journal of Ambient Intelligence and Humanized Computing, Vol. 9, Jun 2018, No 3, pp. 667-677.10.1007/s12652-017-0466-8
- 31. Imran, H., M. Belghis-Zadeh, T. W. Chang, S. Graf. PLORS: A Personalized Learning Object Recommender System. – Vietnam Journal of Computer Science, Vol. 3, February 2016, No 1, pp. 3-13.10.1007/s40595-015-0049-6
- 32. Chen, W., Z. Niu, X. Zhao, Y. Li. A Hybrid Recommendation Algorithm Adapted in e-Learning Environments. – World Wide Web, Vol. 17, March 2014, No 2, pp. 271-284.10.1007/s11280-012-0187-z
- 33. Klašnja-Milićević, A., B. Vesin, M. Ivanović, Z. Budimac. E-Learning Personalization Based on Hybrid Recommendation Strategy and Learning Style Identification. – Computers & Education, Vol. 56, April 2011, No 3, pp. 885-899.10.1016/j.compedu.2010.11.001
- 34. Herlocker, J. L., J. A. Konstan, J. Riedl. Explaining Collaborative Filtering Recommendations. – In: Proc. of 2000 ACM Conference on Computer Supported Cooperative Work, ACM, 2000, pp. 241-250.10.1145/358916.358995
- 35. Luo, X., Y. Xia, Q. Zhu. Applying the Learning Rate Adaptation to the Matrix Factorization Based Collaborative Filtering. – Knowledge-Based Systems, Vol. 37, January 2013, pp. 154-164.10.1016/j.knosys.2012.07.016
- 36. Wu, H., Z. Zhang, K. Yue, B. Zhang, J. He, L. Sun. Dual-Regularized Matrix Factorization with Deep Neural Networks for Recommender Systems. – Knowledge-Based Systems, Vol. 145, April 2018, pp. 46-58.10.1016/j.knosys.2018.01.003
- 37. Zhu, B., F. Ortega, J. Bobadilla, A. Gutiérrez. Assigning Reliability Values to Recommendations Using Matrix Factorization. – Journal of Computational Science, Vol. 26, May 2018, pp. 165-177.10.1016/j.jocs.2018.04.009
- 38. Zhang, Y., M. Chen, D. Huang, D. Wu, Y. Li. iDoctor: Personalized and Professionalized Medical Recommendations Based on Hybrid Matrix Factorization. – Future Generation Computer Systems, Vol. 66, January 2017, pp. 30-35.10.1016/j.future.2015.12.001
- 39. Qiu, L., S. Gao, W. Cheng, J. Guo. Aspect-Based Latent Factor Model by Integrating Ratings and Reviews for Recommender System. – Knowledge-Based Systems, Vol. 110, October 2016, pp. 233-243.10.1016/j.knosys.2016.07.033
- 40. Gemulla, R., E. Nijkamp, P. J. Haas, Y. Sismanis. Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent. – In: Proc. of 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2011, pp. 69-77.10.1145/2020408.2020426
- 41. Gogna, A., A. Majumdar. Balancing Accuracy and Diversity in Recommendations Using Matrix Completion Framework. – Knowledge-Based Systems, Vol. 125, Jun 2017, pp. 83-95.10.1016/j.knosys.2017.03.023
- 42. Tran, T., K. Lee, Y. Liao, D. Lee. Regularizing Matrix Factorization with User and Item Embeddings for Recommendation. – In: Proc. of 27th ACM International Conference on Information and Knowledge Management, ACM, 2018, pp. 687-696.10.1145/3269206.3271730
- 43. Najafabadi, M. K., M. N. R. Mahrin, S. Chuprat, H. M. Sarkan. Improving the Accuracy of Collaborative Filtering Recommendations Using Clustering and Association Rules Mining on Implicit Data. – Computers in Human Behavior, Vol. 67, February 2017, pp. 113-128.10.1016/j.chb.2016.11.010
- 44. Nilashi, M., O. Ibrahi, K. Bagherifard. A Recommender System Based on Collaborative Filtering Using Ontology and Dimensionality Reduction Techniques. – Expert Systems with Applications, Vol. 92, February 2018, pp. 507-520.10.1016/j.eswa.2017.09.058
- 45. Kim, D., B. J. Yum. Collaborative Filtering Based on Iterative Principal Component Analysis. – Expert Systems with Applications, Vol. 28, May 2005, No 4, pp. 823-830.10.1016/j.eswa.2004.12.037
- 46. Benhamdi, S., A. Babouri, R. Chiky. Personalized Recommender System for e-Learning Environment. – Education and Information Technologies, Vol. 22, July 2017, No 4, pp. 1455-1477.10.1007/s10639-016-9504-y
- 47. JJ. MAE and RMSE – Which Metric is Better? Accessed 7 February, 2019. http://medium.com/
- 48. Yu, P., L. Lin, R. Wang, J. Wang, F. Wang. A Unified Latent Factor Correction Scheme for Collaborative Filtering. – In: 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD’14), IEEE, August 2014, pp. 581-586.10.1109/FSKD.2014.6980899
- 49. Datta, A., S. Kovaleva, P. Mardziel, S. Sen. Latent Factor Interpretations for Collaborative Filtering. – arXiv preprint arXiv:1711.10816, 2017.
- 50. Rogers, M., W. Yao, A. Luxton-Reilly, J. Leinonen, D. Lottridge, P. Denny. Exploring Personalization of Gamification in an Introductory Programming Course. – In: Proc. of 52nd ACM Technical Symposium on Computer Science Education, March 2021, pp. 1121-1127.10.1145/3408877.3432402
- 51. Debois, S. 10 Advantages and Disadvantages of Questionnaires. 8 March 2019. https://surveyanyplace.com/blog/questionnaire-pros-and-cons/
- 52. Lefever, S., M. Dal, Á. Matthíasdóttir. Online Data Collection in Academic Research: Advantages and Limitations. – British Journal of Educational Technology, Vol. 38, 2007, No 4, pp. 574-582.10.1111/j.1467-8535.2006.00638.x
- 53. Beiske, B. Research Methods: Uses and Limitations of Questionnaires, Interviews, and Case Studies. – GRIN Verlag, 2007, pp. 1-11.
