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An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System

  • Zeba KhanamEmail author
  • Salwa Alkhaldi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)

Abstract

Completing the school degree and selecting an appropriate university/college for graduation could be a stressful and a confusing venture. Searching for an appropriate college online in a specialized field is a challenging task. This research focusses on building a recommender engine which can classify KSA universities according to the preferences of students and voting from others. The proposed system is a recommender system that depends on differentiating between the preferences/voting of the college and a student profile. The preference/voting of each college is represented as a corpus (dataset) of features (embedded in raw files), specifically the words appearing in the file/document. The student profile is depicted using the same terminology and is constructed by performing the analysis of the content that have been done by the user. In order to propose the model for the Recommendation engine research is conducted on the used algorithms and comparison has also been made of various algorithms in this paper, so as to choose ours, which is random forest with regression.

Keywords

Recommender engine Machine learning Random forest WEKA Weighted clustering Nearest neighbor 

References

  1. 1.
    Kavinkumar, V., Reddy, R.R.: A hybrid approach for recommendation system with added feedback component. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2015). 978-1-4799-8792-4.com/2015/$31.00_c IEEEGoogle Scholar
  2. 2.
    Wang, J., Man, C.: An answer recommendation algorithm for medical community question answering systems (2016). 978-1-5090-2927-3/16/$31.00©2016 IEEEGoogle Scholar
  3. 3.
    Wu, W., Lu, Z.: A personalized recommendation strategy based on trusted social community. In: The 10th International Conference on Computer Science & Education (ICCSE 2015), 22–24 July 2015. Fitzwilliam College, Cambridge University, UK (2015)Google Scholar
  4. 4.
    Rosa, R.L., Rodríguez, D.Z.: Music recommendation system based on user’s sentiments extracted from social networks. IEEE Trans. Consum. Electron. 61(3) (2015). 00983063/15/$20.00©2015 IEEEGoogle Scholar
  5. 5.
    Lath, B.R., Liu, H.: FSOS: a tool for recommending suitable operating systems to computer users. In: SAI Computing Conference 2016, 13–15 July 2016. IEEE, London, UK (2016). 978-1-4673-8460-5/16/$31.00©2016 IEEEGoogle Scholar
  6. 6.
    Parvatikar, S., Joshi, B.: Online book recommendation system by using collaborative filtering and association (2015). 6/15/$31.00©2015 IEEEGoogle Scholar
  7. 7.
    Zeng, J., Li, F.: A restaurant recommender system based on user preference and location in mobile environment. In: 2016 5th IIAI International Congress on Advanced Applied Informatics.  https://doi.org/10.1109/iiai-aai.2016.126. 978-1-4673-8985-3/16 $31.00©2016 IEEE
  8. 8.
    Coelho, B., Costa, F.: HyredHYbrid job recommendation system. Project Work in Team, Portuguese National Strategic Reference Program (QREN 2007–2013), pp 2013/38566Google Scholar
  9. 9.
    Jueajan, B., Naleg, K.: Development of location-aware place recommendation system on android smart phones. In: 2016 Fifth ICT International Student Project Conference (ICT-ISPC) (2016). 978-1-5090-1125-4/16/$31.00©2016 IEEEGoogle Scholar
  10. 10.
    Shino, N., Yamanishi, R.: Recommendation system for alternative-ingredients based on co-occurrence relation on recipe database and the ingredient category. In: 2016 5th IIAI International Congress on Advanced Applied Informatics (2016).  https://doi.org/10.1109/iiai-aai.2016.187. 978-1-4673-8985-3/16$31.00©2016 IEEE
  11. 11.
    Frank, E., Hall, M.A., Witten, I.H.: The WEKAWorkbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Fourth edn. Morgan Kaufmann, Burlington (2016)Google Scholar
  12. 12.
    Tejeda-Lorente, A., Porcel, C., Peis, E., Sanz, R.: A quality based recommender system to disseminate information in a university digital library. Inf. Sci. 261, 52–69 (2014)CrossRefGoogle Scholar
  13. 13.
    Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of E-learning. Knowl.-Based Syst. 22, 261–265 (2009)CrossRefGoogle Scholar
  14. 14.
    Bell, R.M.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), pp. 43–52. IEEE CS, Washington, USA (2005)Google Scholar
  15. 15.
    Bekele, R., Menzel, W.: A Bayesian approach to predict performance of a student (BAPPS): a case with Ethiopian students. In: Artificial Intelligence and Applications, Vienna, Austria, pp. 189–194 (2005)Google Scholar
  16. 16.
    Sebastiani.: Machine learning in automated text categorization. ACM Comput. Surv., 34(1):1–47, Mar. 2002. ISSN 0360-0300. doi:10.1145/ 505282.505283. (2005)Google Scholar
  17. 17.
    Sebastiani: Text categorization in Encyclopedia of database technologies and applications, pp. 683–687. IGI Global (2005)Google Scholar
  18. 18.
    Fong, S., Robert, P.: Automated university admission recommender. ICITA (2009)Google Scholar
  19. 19.
    Reddy, Y.S., Govindarajulu, P.: College recommender system using student’ preferences/voting. IJCSNS 18, 87–98 (2018)Google Scholar
  20. 20.
    Monali, D., Dhanashri, G.: IRJET. College recommendation system for admission. SVPM’s College of engineering Malegaon, Department of Information Technology, Maharashtra, India (2018)Google Scholar
  21. 21.
    Khanam, Z., Agarwal, S.: Map-reduce implementations: survey and performance comparison. Int. J. Comput. Sci. Inf. Technol. 7, 119–126 (2015).  https://doi.org/10.5121/ijcsit.2015.7410
  22. 22.
    Agarwal, S., Khanam, Z.: Map reduce: a survey paper on recent expansion. Int. J. Adv. Comput. Sci. Appl. 6 (2015).  https://doi.org/10.14569/ijacsa.2015.060828
  23. 23.
    Khanam, Z., Ahsan, M.N.: Evaluating the effectiveness of test driven development: advantages and pitfalls. Int. J. Appl. Eng. Res. 12, 7705–7716 (2017)Google Scholar
  24. 24.
    Khanam, Z.: Analyzing refactoring trends and practices in the software industry. Int. J. Adv. Res. Comput. Sci. 10, 0976–5697 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Computing and InformaticsSaudi Electronic UniversityRiyadhSaudi Arabia

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