Personalized Recommendation Based Hashtags on E-learning Systems

The data generated by users on various social structures are growing exponentially over time. They become increasingly prodigious unmanageable and difficult to use. Therefore to easily find the content they produce among this mass of data, users label their own content using neologisms appointed hashtags. This practice attracts more and more the interest of researchers, because beyond the acquisition of knowledge, the Semantic Web approaches are also producing relevant information that may be used in practical situations. In this direction, we thought to exploit the activities of social Web users, mainly Hashtags. Hence, we focused on the identification of hashtags (as well as their different definitions) for personalized recommendation on e-learning systems. This paper aims at giving an insight on the pioneers' works and the opportunities raised by mixing the Social and the Semantic Web for education on one hand. And give the general architecture of our proposition and results obtained on the other hand.