Toward a Recommendation-Oriented Approach Based on Community Detection Within Social Learning Network

The context of this work is around social learning networks through recommendation approaches based on community detection. Indeed, Community detection is considered to be one of the most frequent problems in the social network. Thus, the scope of social networks has known a significant evolution in the last decade, and community detection has emerged to analyse many fields as well as the individual’s interactions within social environments. The main sight of this study is to introduce a recommendation approach based on community detection by focusing on both unipartite and bipartite graphs. We outline some prevailing studies in terms of community detection and recommendation systems, and afterwards we suggest our own approach. Therefore, the challenge is defined as highlighting an approach for detecting learners that interact mutually and share the same interests towards content in order to provide relevant recommendations.