Recommender system for elearning through content And profile based approach using spal model

Abstract

Elearning environments have become a way of life The aim of the newlinerecommender systems is to suggest an optimal set of modules that satisfy the needs of newlinethe user on a particular topic Recommender systems research has used advances in newlinelearning styles personal preferences of the users and tests of ability to suggest newlinecontent These suffer from two problems gap in content matching and lack of newlinecontext The domain of information retrieval has shown us that the gap between newlineperceived results and intended results is significant This is due to complexity of the newlinecontent Hence the recommender systems need advanced mechanisms for content newlinetagging and management as a part of their repertoire The content management newlineaspects need to look beyond the document management style or the data management newlineaspects, and instead focus on the content tagging for modeling This is a significant newlinechallenge newline newline