Weighting based approach for learning resources recommendations

Outmane Bourkoukou - Cadi Ayyad University, Marrakesh, Morocco
Omar Achbarou - Cadi Ayyad University, Marrakesh, Morocco

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.2.3.124


Personalized e-learning systems based on recommender systems refines enormous amount of data and provides suggestions on learning resources which is appealing to the learner. Although, the recommender systems depends on content based approach or collaborative filtering technique to make recommendations, these methods suffers from cold start and data sparsity problems. To overcome the limitations of the aforementioned problems, a weight based approach is proposed for better performance. The main criterion for building a personalized recommender system is to exploit useful content and provide better recommendations with minimal processing time. The proposed system is a web based client side application which uses user profiles to form neighborhoods and calculates predictions using weights. For newcomers a profile is constructed based on learning styles. The resources which might be of interest to the user are predicted from calculated predictions.


E-learning; Recommender system; Data sets; Collaborative filtering

Full Text:



M. Anderson, M. Ball, H. Boley, S. Greene, N. Howse, et al., RACOFI: A Rule-Applying Collaborative Filtering System, in Proceedings of IEEE/WIC international conference on web intelligence/intelligent agent technology, Halifax, Canada. 13-23, 2003.

H. Avancini, U. Straccia, User Recommendation for Collaborative and Personalised Digital Archives, Int. J. Web Based Communities, vol. 1(2), pp. 163-175, 2005.

O. Bourkoukou, E. El Bachari, E-learning personalization based on collaborative filtering and learner’s preference, Journal of Engineering Science and Technology, vol. 11 (11), pp. 1565-1581, 2016.

O. Bourkoukou, E. El Bachari, M. El Adnani, Arabian Journal for Science and Engineering, A Recommender Model in E-learning Environment, vol. 42( 2), pp. 607–617, 2017.

P. Brusilovsky, W. Nejdl, Adaptive Hypermedia and Adaptive Web. In: M. P. Singh (ed.) Practical Handbook of Internet Computing (pp. 1-14.). Baton Rouge: Chapman Hall & CRC Press, 2005.

M. Dascalua, J. M. Bodea I., C. Moldoveanuc N., A. Mohoraa, M. Lytras, et al. () A recommender agent based on learning styles for better virtual collaborative learning experiences. Comput Hum Behav, 45, 243–253, 2015.

R. Felder, L. Silverman, Learning and Teaching Styles in Engineering Eductaion, Engineering Education, 78: 674-684, 1988.

R. Gluga, J. Kay, T. Lever, Modeling long term learning of generic skills, Intelligent Tutoring Systems, vol. 1, pp. 85-94, 2010.

T. Hofmann, Latent Semantic Models for Collaborative Filtering, ACM Transactions on Information Systems , vol. 22, pp. 89-115, 2004.

H. Imran, M. Belghis-Zadeh, T. Chang, K. Graf S., PLORS: a personalized learning object recommender system. Vietnam J. Comput. Sci. vol. 3(1), pp. 3–13, 2016.

O. Kaššák, M. Kompan, M. Bieliková, Personalized hybrid recommendation for group of users: Top-N multimedia recommender, Information Processing & Management, 52(3): 459-477, 2016.

M. Khribi K., M. Jemni, O. Nasraoui, Recommendation systems for personalized technology-enhanced learning. In: Ubiquitous learning environments and technologies, pp. 159–180. Springer, Berlin, 2015.

A. Klašnja-Milićević, M. Ivanović, A. Nanopoulos, Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, vol. 44(4), pp. 571-604, 2015.

A. Klašnja-Milićević, B. Vesin, M. Ivanović, Z. Budimac, E-Learning personalization based on hybrid recommendation strategy and learning style identification, Computers & Education, 56(3): 885-899, 2011.

V. Kumar, J. Nesbit, K. Han, Rating learning object quality with distributed Bayesian belief networks: the why and the how, in Advanced Learning Technologies, 2005. ICALT 2005. Fifth IEEE International Conference : 685-687, 2005.

Li, Y., Zheng, Y., Kang, J. & Bao, H. (2016) Designing a Learning Recommender System by Incorporating Resource Association Analysis and Social Interaction Computing. In: Li Y. et al. (eds) State-of-the-Art and Future Directions of Smart Learning. Lecture Notes in Educational Technology. Springer, Singapore.

N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel, R. Koper, Recommender systems in technology enhanced learning, in: P.B. Kantor, F. Ricci, L. Rokach, B.Shapira (Eds.), Recommender Systems Handbook, pp. 387–415, 2011.

C. Olga S., G. Jesus B., P. Diana, Extending web-based educational systems with personalised support through User Centred Designed recommendations along the e-learning life cycle, Science of Computer Programming, vol. 88, pp. 92-109, 2014.

M. Recker, A.Walker, D. Wiley, An interface for collaborative filtering of educational resources, in In Proceedings of the 2000 International Conference on Artificial Intelligence (IC-AI'2000): 317-323, 2000.

M. Recker M., A. Walker, Supporting 'word-of-mouth' social networks via collaborative information filtering, Journal of Interactive Learning Research, vol. 14 (1), pp. 79-98, 2003.

T. Tang, G. McCalla, Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment., International Journal on E-Learning, 4 (1), 105-129, 2005.

J. Tarus K., Z. Niu, D. Kalui, A hybrid recommender system for e-learning based on context awareness and sequential pattern mining, Soft computing, pp. 1–13, 2017.

F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh, Recommendation systems: Principles, methods and evaluation, Egyptian Informatics Journal,vol. 16(3), pp. 261-273, 2015.

M. Mısır, M. Sebag, Alors: An algorithm recommender system, Artificial Intelligence, vol. 244, , pp. 291-314, 2017.

Z. Sun, L. Han, W. Huang, X. Wang, S. Zeng, M. Wang, H. Yan, Recommender systems based on social networks, Journal of Systems and Software, vol. 99, pp. 109-119, 2015