A framework to alleviate common problems from recommender system : A case study for technical course recommendation

References

• Moreno, N Maria Segrera , SaddysLopez, F Vivian and Munoz, Maria Dolores and Sanchez, Angel Luis (2016), “Web mining based framework for solving usual problems in recommender systems. A case study for movies’ recommendation” in Neurocomputing Vol. 176, Elsevier,pp. 72—80.
   Google Scholar
• Zhang, Xi and Cheng, JianQiu , Shuang Zhu , GuiboLu, Hanqing ( 2015) , “DualDS: A dual discriminative rating elicitation framework for cold start recommendation” in Knowledge-Based Systems, Vol. 73, Elsevier, pp. 161-172
   Google Scholar
• Pirasteh, Parivash Hwang , DosamJung, Jason J (2015),”Exploiting matrix factorization to asymmetric user similarities in recommendation systems “in Knowledge-Based Systems, Vol. 83, Elsevier,pp. 51–57
   Google Scholar
• Gediminas Adomavicius and Jingjing Zhang (2015),”Improving stability of recommender systems: A meta-algorithmic approach”, in IEEE Transactions on Knowledge and Data Engineering, Vol. 27, IEEE, pp. 1573-1587
   Google Scholar
• Zhao, X. , Niu, Z. , Wang, K. , Niu, K. , & Liu, Z. (2015). Improving top- N recommendation performance using missing data. Mathematical Problems in Engineering , 2015.
   Web of Science ®Google Scholar
• Morente-Molinera, J. A. , Kou, G. , González-Crespo, R. , Corchado, J. M. , & Herrera-Viedma, E. (2017). Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowledge-Based Systems, 137, 54-64. doi: 10.1016/j.knosys.2017.09.010
   Web of Science ®Google Scholar
• Makwana, K. , Patel, J. , & Shah, P. (2017, March). An Ontology Based Recommender System to Mitigate the Cold Start Problem in Personalized Web Search. In International Conference on Information and Communication Technology for Intelligent Systems (pp. 120-127). Springer, Cham.
   Google Scholar
• Singh, P. , Ahuja, S. and Jain, S. , 2019. Latest Trends in Recommender Systems 2017. In Advances in Data and Information Sciences (pp. 197-210). Springer, Singapore.
   Google Scholar
• Kallio, R. E. (1995). Factors influencing the college choice decisions of graduate students. Research in Higher Education, 36(1), 109-124. doi: 10.1007/BF02207769
   Web of Science ®Google Scholar
• DesJardins, S. L. , Dundar, H. , & Hendel, D. D. (1999). Modeling the college application decision process in a land-grant university. Economics of Education Review, 18(1), 117-132. doi: 10.1016/S0272-7757(98)00023-5
   Web of Science ®Google Scholar
• Ahmad, S. Z. , & Hussain, M. (2017). An investigation of the factors determining student destination choice for higher education in the United Arab Emirates. Studies in Higher Education, 42(7), 1324-1343. doi: 10.1080/03075079.2015.1099622
   Web of Science ®Google Scholar
• Pugh, D. L. (2017). Factors Affecting African-American Enrollment and Intent to Enroll in an Advanced Placement Program in a Suburban High School.
   Google Scholar
• Settouti, N. , Bechar, M. E. A. , & Chikh, M. A. (2016). Statistical comparisons of the top 10 algorithms in data mining for classification task. International Journal of Interactive Multimedia and Artificial Intelligence , 4(1), 46-51 doi: 10.9781/ijimai.2016.419
   Web of Science ®Google Scholar
• Cheng, J. , & Greiner, R. (1999, July). Comparing Bayesian network classifiers. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence (pp. 101-108). Morgan Kaufmann Publishers Inc.
   Google Scholar
• Albayati, M. , & Issac, B. (2015). Analysis of intelligent classifiers and enhancing the detection accuracy for intrusion detection system. International Journal of Computational Intelligence Systems , 8 (5), 841-853. doi: 10.1080/18756891.2015.1084705
   Web of Science ®Google Scholar
• Ritchey, R. P. , Shearer, G. G. , & Renard, K. D. (2019). Nave Bayes Log File Reduction and Analysis (No. ARL-TR-8624). US Army Research Laboratory Aberdeen Proving Ground United States.
   Google Scholar
• Cunningham, P. , & Delany, S. J. (2007). k-Nearest neighbour classifiers. Multiple Classifier Systems, 34(8), 1-17.
   Google Scholar
• Lee, I. , Kwak, M. , & Han, D. (2016). A dynamic k-nearest neighbor method for WLAN-based positioning systems. Journal of Computer Information Systems , 56(4), 295-300. doi: 10.1080/08874417.2016.1164000
   Web of Science ®Google Scholar
• Zhang, S. , Li, X. , Zong, M. , Zhu, X. , & Wang, R. (2018). Efficient knn classification with different numbers of nearest neighbors. IEEE transactions on neural networks and learning systems , 29(5), 1774-1785. doi: 10.1109/TNNLS.2017.2673241
   PubMed Web of Science ®Google Scholar
• Schapire, R. E. (2013). Explaining adaboost. In Empirical inference (pp. 37-52). Springer, Berlin, Heidelberg.
   Google Scholar
• Zhang, P. B. , & Yang, Z. X. (2016). A novel adaboost framework with robust threshold and structural optimization. IEEE transactions on cybernetics , 48(1), 64-76. doi: 10.1109/TCYB.2016.2623900
   PubMed Web of Science ®Google Scholar
• Lewis, R. J. (2000, May). An introduction to classification and regression tree (CART) analysis. In Annual meeting of the society for academic emergency medicine in San Francisco, California (Vol. 14).
   Google Scholar
• Van Aardt, J. A. N. , & Norris-Rogers, M. (2008). Spectral–age interactions in managed, even-aged Eucalyptus plantations: application of discriminant analysis and classification and regression trees approaches to hyperspectral data. International Journal of Remote Sensing , 29(6), 1841-1845. doi: 10.1080/01431160701874546
   Web of Science ®Google Scholar
• Bauer, E. , & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning, 36(1-2), 105-139. doi: 10.1023/A:1007515423169
   Web of Science ®Google Scholar
• Puri, Vikram , Anand Nayyar , and Linesh Raja . “Agriculture drones: A modern breakthrough in precision agriculture.” Journal of Statistics and Management Systems 20.4 (2017): 507-518. doi: 10.1080/09720510.2017.1395171
   Web of Science ®Google Scholar
• Lee, S. J. , Xu, Z. , Li, T. , & Yang, Y. (2018). A novel bagging C4. 5 algorithm based on wrapper feature selection for supporting wise clinical decision making. Journal of biomedical informatics , 78, 144-155. doi: 10.1016/j.jbi.2017.11.005
   PubMed Web of Science ®Google Scholar
• Ma, B. L. W. H. Y. , Liu, B. , & Hsu, Y. (1998, August). Integrating classification and association rule mining. In Proceedings of the fourth international conference on knowledge discovery and data mining (pp. 24-25).
   Google Scholar