Enabling recommendation system architecture in virtualized environment for e-learning

E-learning sites are useful for improving the skills and awareness of the academic backbone, such as instructors, students, administrative staff, and those who are searching for current information about various educational institutes. Despite all the benefits of an online learning platform, users face some challenges and complexities, such as selecting appropriate learning material and courses based on their needs and preferences. Hence, the provision of quality resources during the training phases is their central responsibility, the lack of online assistance offered by service providers is known to be the key cause of many difficulties. There is a need to create a system that can intelligently propose courses while considering a variety of viewpoints to enhance the learners' skills and knowledge. This research proposes an architecture that builds semantic recommendations with the aid of virtual agents based on user requirements and preferences, assisting academia in seeking appropriate courses in a real-world setting. The experimental and statistical results show that, when compared with existing techniques, the virtualized agent-based recommendation system not only improved user learning skills but also made course selection easier, depending on users’ interests and preferences.