Recommendation within e-learning environments is not a new issue, it is even in the heart of reflection of researchers in this topic. In this paper, we outline our architecture for multidimensional recommendation in the e-learning environments, focused on personalized recommendation. For this purpose, we provide methodology for retrieving and analyzing hashtags contained in users’ writings on social networks called a personomy, to enrich automatically their profiles. We have defined a method to identify semantics of hashtags and semantic relationships between the meanings of different hashtags using the data mining and semantic Web technologies. By the way, we have defined the concept of Folksionary, as a hashtags dictionary that for each hashtag clusters its definitions into meanings. Semantized hashtags are thus used to feed the learner’s profile so as to make personalized recommendation on learning systems.