Social Recommender Systems in E-Learning Environments: A Literature Review

Since the beginning of the COVID-19 pandemic, e-learning platforms have received a lot of attention as a way to acquire knowledge remotely. These platforms are not only valuable to students in educational institutions, but also to individuals looking to grow personally and professionally. However, the plethora of learning materials and resources available online creates a problem of information overload. Due to this problem, it is often difficult for learners to find relevant and appropriate information in this vast amount of content. To address this challenge, the integration of recommender systems is crucial to improve the effectiveness of e-learning platforms. In this regard, several methods have been presented by the research community. These methods include the fundamental approaches based on content filtering, collaborative filtering-based approaches, and hybrid ones. Social recommender systems are designed to simplify and enhance an individual’s learning journey. This paper provides an overview of the current state of social recommender systems in online learning environments and examines the different approaches and capabilities used in these systems.