Initialization of Matrix Factorization Methods for University Course Recommendations Using SimRank Similarities

  • Alisa Krstova
  • Bozhidar StevanoskiEmail author
  • Marija Mihova
  • Vangel V. Ajanovski
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 940)


The accurate estimation of students’ grades in prospective courses is important as it can support the procedure of making an informed choice concerning the selection of next semester courses. As a consequence, the process of creating personal academic pathways is facilitated. This paper provides a comparison of several models for future course grade prediction based on three matrix factorization methods. We attempt to improve the existing techniques by combining matrix factorization with prior knowledge about the similarity between students and courses calculated using the SimRank algorithm. The evaluation of the proposed models is conducted on an internal dataset of anonymized student record data.


Course recommendation engine Study plan development Collaborative filtering Matrix factorization 



This work is a result within the project SISng (Study Information Systems of the Next Generation) [11], which is currently ongoing at the Faculty of Computer Science and Engineering in Skopje. The authors would also like to thank Ljupcho Rechkoski for the provided materials.


  1. 1.
    Ajanovski, V.V.: Guided exploration of the domain space of study programs: recommenders in improving student awareness on the choices made during enrollment. In: Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (INTRS17), pp. 43–47. CEUR-WS, Como (2017)Google Scholar
  2. 2.
    Carballo, F.O.G.: Masters Courses Recommendation: Exploring Collaborative Filtering and Singular Value Decomposition with Student Profiling, Instituto Superior Tecnico, Lisboa (2014).
  3. 3.
    Hu, Q., Polyzou, A., Karypis, G., Rangwala, H.: Enriching course-specific regression models with content features for grade prediction. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 504–513 (2017)Google Scholar
  4. 4.
    Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM Press, Edmonton (2002)Google Scholar
  5. 5.
    Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS 2007, Vancouver, British Columbia, Canada, pp. 1257–1264 (2008)Google Scholar
  6. 6.
    Morsy, S., Karypis, G.: Cumulative knowledge-based regression models for next-term grade prediction. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 552–560. SIAM, Houston (2017)Google Scholar
  7. 7.
    O’Mahony, M.P., Smyth, B.: A recommender system for on-line course enrollment: an initial study. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 133–136. ACM, New York (2007)Google Scholar
  8. 8.
    Polyzou, A., Karypis, G.: Grade prediction with models specific to students and courses. Int. J. Data Sci. Anal. 2(3–4), 159–171 (2016)CrossRefGoogle Scholar
  9. 9.
    Rechkoski, L., Ajanovski, V.V., Mihova, M.: Evaluation of grade prediction using model-based collaborative filtering methods. In: 2018 IEEE Global Engineering Education Conference (EDUCON), pp. 1096–1103. IEEE, Tenerife (2018).
  10. 10.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887. ACM, New York (2008)Google Scholar
  11. 11.
    Student Information System of the Next Generation (2009/2018).
  12. 12.
    Symeonidis, P., Zioupos, A.: Matrix and Tensor Factorization Techniques for Recommender Systems. Springer, Cham (2016). Scholar
  13. 13.
    Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008). Scholar
  14. 14.
    Zhuhadar, L., Nasraoui, O., Wyatt, R., Romero, E.: Multi-model ontology-based hybrid recommender system in e-learning domain. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, pp. 91–95. IEEE, Milan (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringSs. Cyril and Methodius UniversitySkopjeMacedonia

Personalised recommendations