Miftahul Jannat MOKARRAMA, Sumi KHATUN, Mohammad Shamsul AREFIN

A content-based recommender system for choosing universities

Recommender system (RS) is a knowledge discovery and decision-making system that has been extensively used in a myriad of applications to assist people in making distinct choices from vast sources. This paper proposes a recommendation system that will help the prospective students of Bangladesh in choosing the most suitable private universities for getting admission. Since selecting the best private university does not depend merely on a few criteria or choices and making a decision considering all those criteria is not an easy task, a recommendation system can be of great assistance in this scenario for the prospective students. In this proposed recommendation system a list of top-K private universities is recommended to the students who are willing to get admitted to the private universities using content-based filtering technique. To attain this goal we considered six parameters, namely grade point average (GPA) of secondary school certificate (SSC) examination, GPA of higher secondary certificate (HSC) examination, total GPA, tuition fees, university ratings, and university rankings. Finally, we evaluated the system with a total of 947 real feedback from prospective students and obtained the accuracies of 89.05%, 95.85%, 48%, 92.32%, and 71.93% using 5 different performance metrics: precision, recall, specificity, F1 score, and balanced accuracy, respectively.

PDF

___

[1] Ricci F, Rokach L, Bracha S, Kantor PB. Recommender Systems Handbook. Boston, USA: Springer, 2011.
[2] Lai Y, Zeng J. A cross-language personalized recommendation model in digital libraries. Electronic Library of the Institute of Scientific and Technical Information of China 2013; 31 (3): 264-277. doi: 10.1108/EL-08-2011-0126
[3] Mokarrama MJ, Arefin MS. RSF: A recommendation system for farmers. In: IEEE Region 10 Humanitarian Technology Conference (R10-HTC); Dhaka, Bangladesh; 2017. pp. 843-850.
[4] Rahman T, Nakata S, Nagashima Y, Rahman MM, Sharma U et al. Bangladesh Tertiary Education Sector Review: Skills and Innovation for Growth. USA: World Bank, 2019.
[5] Ali M, Hossain ME, Aktar J. The impact of socioeconomic status on selecting post-secondary institutions: The case of private universities in Bangladesh. European Journal of Education Studies 2018; 5 (8): 185-206. doi: 10.5281/ zenodo.2483325
[6] Monem M, Baniamin HM. Higher education in Bangladesh: Status, issues and prospects. Pakistan Journal of Social Sciences (PJSS) 2010; 30 (2): 293-305.
[7] Wadood SN. The Public-Private University Debate in the Higher Education Sector in Bangladesh. Munich, Ger- many: Munich Personal RePEc Archive, Munich University Library, 2008.
[8] Nobi MN. A comparative study of the socioeconomic profile of public and private university students in Bangladesh. The Chittagong University Journal of Social Sciences 2012; 30 (1): 43-56.
[9] Jamil MAA, Sarker MM, Abdullah M. Students’ choice criteria to select a private university for their higher education in Bangladesh. European Journal of Business and Management 2012; 4 (17): 177-185.
[10] Saif ANM, Nipa N, Siddique MA. Assessing the factors behind choosing universities for higher education: A case of Bangladesh. Global Journal of Management and Business Research 2016; 16 (11): 45-51.
[11] Isynkaye FO, Folajimi YO, Ojokoh BA. Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal 2015; 16 (3): 261-273. doi: 10.1016/j.eij.2015.06.005
[12] Pazzani MJ, Billsus D. Content-based recommendation systems. In: Brusilovsky P, Kobsa A, Nejdl W (editor). The Adaptive Web: Methods and Strategies of Web Personalization. 1st ed. Berlin, Heidelberg: Springer, 2007, pp. 325-341.
[13] Lops P, de Gemmis M, Semeraro G. Content-based recommender systems: State of the art and trends. In: Ricci F, Rokach L, Bracha S, Kantor PB (editor). Recommender Systems Handbook. 1st ed. Boston, USA: Springer, 2011, pp. 73-105.
[14] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the- art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 2005; 17 (6): 734-749. doi: 10.1109/TKDE.2005.99
[15] Bhatia T. User based collaborative filtering recommendation system using Apache Mahout. MS, Georgia Institute of Technology, Atlanta, GA, USA, 2016.
16] Burke R. Hybrid web recommender systems. In: Brusilovsky P, Kobsa A, Nejdl W (editor). The Adaptive Web: Methods and Strategies of Web Personalization. 1st ed. Berlin, Heidelberg: Springer, 2007, pp. 377-408.
[17] Ragab AHM, Mashat AFS, Khedra AM. Design and implementation of a hybrid recommender system for predicting college admission. International Journal of Computer Information Systems and Industrial Management Applications 2014; 6: 35-44.
[18] Wakil K, Akram B, Kamal N, Safi A, Web recommender system for private universities’ admission in Iraq: UHD case study. International Journal of e-Education, e-Business, e-Management and e-Learning 2014; 4 (5): 329-340. doi: 10.7763/ijeeee.2014.v4.348
[19] Fong S, Biuk-Aghai RP. An automated university admission recommender system for secondary school students. In: The 6th 29 International Conference on Information Technology and Applications; New York, USA; 2009. pp. 1-20.
[20] Monali MD, Dhanashri MG, Dipali MJ, Tejaswini MK, Nale RK. College recommendation system for admission college. International Research Journal of Engineering and Technology 2018; 5 (3): 1269-1272.