Abstract
The social learning network is regarded as one of the most widespread types of online learning due to its collaborative and interactive properties. Recommendation systems contribute significantly in delivering relevant contents on social networks. However, learners are not consistently engaged. It is inescapable to develop a strategy for providing recommendations to meet system requirements for non-considerably active periods and to interrelate all events. To mitigate this problem, we propose an approach to fill the information gap and make recommendations more reliable. Our approach therefore introduces community detection and the correlation between data related to all events carried out by learners: consultations, information sharing, discussions, researches, etc. It is based on (a) detecting communities of learners who interact more intensely with each other and share common interests, and (b) calculating recommendations based on communities detected and computed correlations between data associated to learners’ events. Our perspective allows us to jointly embed two crucial guidelines, which are the event correlation and community detection.
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References
Che, Y.-K., Hörner, J.: Recommender systems as mechanisms for social learning. Q. J. Econ. 133(2), 871–925 (2018)
Sikka, R., Dhankhar, A., Rana, C.: A survey paper on e-learning recommender system. Int. J. Comput. Appl. 47(9), 27–30 (2012)
Bourkoukou, O., El Bachari, E., El Adnani, M.: A recommender model in e-learning environment. Arab. J. Sci. Eng. 42(2), 607–617 (2017)
Lalwani, D., Somayajulu, D.V.L.N., Krishna, P.R.: A community driven social recommendation system. In: 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, pp. 821–826 (2015)
Dwivedi, P., Bharadwaj, K.K.: e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst. 32(2), 264–276 (2015)
Dahdouh, K., Dakkak, A., Oughdir, L., Ibriz, A.: Association rules mining method of big data for e-learning recommendation engine. In: Advanced Intelligent Systems for Sustainable Development (2018)
Gasparetti, F., Micarelli, A., Sansonetti, G.: Community detection and recommender systems. In: Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining, pp. 1–14. Springer, New York (2017)
Gorripati, S.K., Vatsavayi, V.K.: Community-based collaborative filtering to alleviate the cold-start and sparsity problems. Int. J. Appl. Eng. Res. 12(15), 9 (2017)
Souabi, S., Retbi, A., Idrissi, K., Bennani, S.: Toward a recommendation-oriented approach based on community detection within social learning network. In: Advanced Intelligent Systems for Sustainable Development (2019). In the process of publication
Sunitha, M., Adilakshmi, T.: Session aware music recommendation system with user-based and item-based collaborative filtering method. Int. J. Comput. Appl. 96(24), 22–27 (2014)
Fazeli, S., Loni, B., Drachsler, H., Sloep, P.: Which recommender system can best fit social learning platforms? In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds.) Open Learning and Teaching in Educational Communities, vol. 8719, pp. 84–97. Springer, Cham (2014)
Manouselis, N., Drachsler, H., Verbert, K., Duval, E.: Recommender Systems for Learning. Springer, New York (2013)
Kowald, D., Lacic, E., Theiler, D., Lex, E.: AFEL-REC: a recommender system for providing learning resource recommendations in social learning environments. arXiv:1808.04603 [cs], août 2018
Fazazi, H.E., Qbadou, M., Salhi, I., Mansouri, K.: Personalized recommender system for e-Learning environment based on student’s preferences, p. 6 (2018)
Baidada, M., Mansouri, K., Poirier, F.: Hybrid recommendation approach in online learning environments. In: Rocha, Á., Serrhini, M. (eds.) Information Systems and Technologies to Support Learning, vol. 111, pp. 39–43. Springer, Cham (2019)
Bouihi, B., Bahaj, M.: A semantic web architecture for context recommendation system in e-learning applications. In: Ben Ahmed, M., Boudhir, A.A. (eds.) Innovations in Smart Cities and Applications, vol. 37, pp. 67–73. Springer, Cham (2018)
Souali, K., Rahmaoui, O., Ouzzif, M.: Introducing a traceability based recommendation approach using chatbot for e-learning platforms. In: Ezziyyani, M. (ed.) Advanced Intelligent Systems for Sustainable Development, vol. 915, pp. 346–357. Springer, Cham (2019)
Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 421–451. Springer, Boston (2015)
Cerna, M.: Modified recommender system model for the utilized eLearning platform. J. Comput. Educ. 6, 1–25 (2019)
Doja, M.N.: Recommender system based on web usage mining for restructuring of e-learning websites and blogs, vol. 5, no. 1, p. 8 (2017)
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Souabi, S., Retbi, A., Idrissi, M.K., Bennani, S. (2020). A Recommendation Approach Based on Community Detection and Event Correlation Within Social Learning Network. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_8
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DOI: https://doi.org/10.1007/978-3-030-36778-7_8
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