An E-learning recommender system using switch hybridization

Abstract

A recommender system is developed to improve the efficient performance of new learners using stereotype filtering and collaborative filtering. The paper aimed at adopting a model that follows a classification of recommendation based on information sources that are being used, such as user profile and user-item profile to make accurate recommendation to new learners. The system undertakes stereotype filtering because it is less sensitive to the new learner. In stereotype filtering, recommendations are issued to new user by first identifying the category they belongs to and locating preferences of other users in the same category. The proposed system combines both recommendation techniques in order to gain better performance and address the shortcomings of each. The result showed that the satisfaction level are based on learner academic Information. Privacy issues should be explored in gaining learner’s loyalty as well as frequent feedback. Hence, there is need for developing additional facilities to preserve privacy information and at the same time be able to use them for accurate recommendation.