The Recommending Courses based on the Similarity of Students’ Preferences

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    To keep students in touch, they need to be caught through proposing different courses similar to the ones they are interested in. The present paper is going to discuss the idea of recommending courses and how it will be applied into student profiles as the result of clustering the books or courses as well as the student preferences. It will illustrate the way the item and student profiles enable to recommend courses in our system.

     

     


  • Keywords


    Recommending courses, clustering, student preferences, profiles

  • References


      [1] Sarwar, B. 2001. Item-based collaborative filtering recommendation algorithms. In: WWW 10 Conference, pp. 285–295.

      [2] Liu, D. and Y. Shih. 2005. Integrating AHP and Data Mining for Product Recommendation Based on Customer Lifetime Value, Information and Management 42(3), 387-400.

      [3] Horozov, T., N., Narasimhan and V. Vasudevan. 2006. Using location for personalized POI recommendations in mobile environments. In: SAINT. pp. 124–129.

      [4] Breese, J., D., Heckerman and C. Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the 14th UAI.

      [5] Yang, Li, B., Q. Yang and X. Xue. 2009. Transfer learning for collaborative filtering via a rating-matrix generative model. In: ICML, pp. 617–624.

      [6] Marlin, B. Modeling user rating profiles for collaborative filtering. in NIPS’03

      [7] Wang, F. and H. Shao. 2004. Effective Personalized Recommendation Based on Time-Framed Navigation Clustering and Association Mining. Expert Systems with Application 27(3), 365-377.

      [8] Banerjee, A., D. Inderjit, G. Joydeep, M. Srujanaand S. Dharmendra. 2007. A generalized maximum entropy approach to Bergman co-clustering and matrix approximation. in KDD ’04, pp. 509–514.

      [9] Indulska, J., L.T. Yang, T. Ungererand J. Cao (Eds.). 2007. UIC 2007, LNCS 4611, pp. 1130–1139.Springer-Verlag Berlin Heidelberg.

      [10] Ekstrand, M. D., J.T. Riedl and J. A. Konstan. 2011. Collaborative Filtering Recommender Systems. Foundations and Trends in Human–Computer Interaction 4(2), pp 81-173.

      [11] Tung, H. and V. Soo. 2004. A personalized restaurant recommender agent for mobile e-service. In: IEEE International Conference on e-Technology. E-Commerce and e-Service. pp. 259–262.

      [12] Fu, J. and J. Ye. 2017. Simplified neutrosophic exponential similarity measures for the initial evaluation/diagnosis of benign prostatic hyperplasia symptoms 9, 154.

      [13] Korenius, T. J., Laurikkala and M. Juhola. 2007. On principal component analysis, cosine and Euclidean measures in information retrieval. Doi:10.1016/j.ins.2007.05.027

      [14] Andrew, I. S., P. Alexandrin, H. U. Lyle and M.P. David. 2002. Methods and Metrics for Cold-Start Recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York City, New York: ACM. pp. 253–260. ISBN 1-58113-561-0. Retrieved 2018-02-02.

      [15] Lieberman, H. 1995. Letizia: An Agent that Assists Web Browsing. In proceedings of the 1995 International Joint Conference on Artificial Intelligence. Montreal, Canada.

      [16] El Emary, I. and A. Brzozowska. 2017. Shaping the Future of ICT. Boca Raton: CRC Press. Chapter 6 pages Artificial Intelligence in E-Learning.

      [17] Alaoui, H.H., E. Hachem and C. Ziti. 2016. Data mining for the service of intelligent tutoring system. Int.J.Mult.disc.scie 1(1): 61 -65.

      [18] Shahab. S.S., S. Jamshed and A. Rashid. 2017. A Novel Approach for Book Recommendation using Fuzzy based Aggregation, Indian Journal of Science and Technology 10(19), DOI: 10.17485/ijst/2017/v10i19/96787.

      [19] Koyutürk, M., Y. Kim, S. Subramaniam. S. Wojciech and G. Ananth. 2006. Detecting conserved interaction patterns in biological networks. J ComputBiol 13:1299–322.

      [20] Manda, P., S. Ozkan, H. Wang, F. McCarthy, and S.M. Bridges. 2012. Cross-ontology multi-level association rule mining in the gene ontology. PLoS One 7: e47411.

      [21] Agrawal, R., T. Imielinski and A. Swami. 1993. Mining Association Rules Between Sets of Items in Large Databases. SIGMOD Conference. pp. 207-216.


 

View

Download

Article ID: 23244
 
DOI: 10.14419/ijet.v7i4.32.23244




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.