When Hashtags Meet Recommendation in e-learning Systems
Merieme GHENNAME1, Mounia ABIK2, Julien SUBERCAZE1, Christophe GRAVIER1, Frederique LAFOREST1, Rachida AJHOUNE2
Citation : Merieme GHENNAME,et.al, When Hashtags Meet Recommendation in e-learning Systems International Journal of Innovative Research in Electronics and Communications 2015, 2(6) : 15-28
Knowledge, education and learning are major concerns in today's society. The technologies for human learning aim to promote, stimulate, support and validate the learning process. Our approach explores the opportunities raised by mixing the Social Web and the Semantic Web technologies for e-learning. More precisely, we work on enriching learner's profiles from their activities on the social Web. We propose a methodology for exploiting hashtags contained in users' writings for the automatic enrichment of learner's profiles. This paper aims at giving an insight on the processing required on hashtags before being source of knowledge on the user interests. For this purpose we introduce our approach for the automatic structuration of hashtags definitions into synonym rings. We present the output as a so-called folksionary, i.e. a single integrated dictionary built from everybody's definitions. Semantized hashtags are thus used to feed the learner's profile and particularly the focus field.