Manuscript received August 6, 2023; revised August 30, 2023; accepted September 20, 2023; published March 4, 2024
Abstract—In recent years, informal education has witnessed a significant upsurge, fueled by technological advancements and the ubiquitous availability of online educational content. Internet users, including students, researchers, and teachers, are increasingly seeking supplementary educational resources across diverse online repositories to augment their knowledge. Within this landscape, recommendation systems emerge as indispensable tools, aiding users in the discovery of pertinent resources aligned with their academic interests. This article proposes a novel recommendation methodology leveraging a hybrid approach, incorporating both Content-Based Filtering (CBF) and Collaborative Filtering (CF) algorithms. By harnessing information from a myriad of data repositories, this system excels in identifying and presenting the most relevant and desirable educational resources, with a particular focus on meeting the needs of students. This holistic approach embraces user profiles, contextual information, and supplementary data, underscoring its potential to revolutionize informal education in the digital age.
Keywords—recommendation systems, hybrid filtering, e-learning, informal education
Cite: Mohamed Timmi, Loubna Laaouina, Adil Jeghal, Said El Garouani, and Ali Yahyaouy, "Educational Video Recommender System," International Journal of Information and Education Technology vol. 14, no. 3, pp. 362-371, 2024.