WITHDRAWN: Enriching ‘user item rating matrix’ with resource description framework for improving the accuracy of recommendation in E-learning environment

Abstract

Abstract Recommender systems have been successfully used in a wide variety of domains. They predict, rank and recommend items to users. The prediction is based on user's preferences in the form of ratings over a set of items stored in the 'user-item rating matrix'. However, often this matrix does not have sufficient ratings to make good quality of recommendations as most of the users do not provide ratings for the items that they have consumed. This situation leads to 'sparsity' by which a majority of traditional recommender systems are suffering. Although existing works on recommender systems have attempted to address 'sparsity', most of them are unable to take advantage of 'resource description framework' and 'Jena engine' to obtain additional preferences of learners implicitly from Moodle server. Hence, in this paper, we propose an enhanced course recommendation framework to enrich the 'sparse rating matrix' for improving the accuracy of recommendations. Experimental results on the 'enriched item rating matrix' show the effectiveness of the proposed approach and the importance of these semantic tools.