Goal-based Framework for cold-start problem using multi-user personalized similarities in e-Learning scenarios

This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering because only traditional content-based filtering is not sufficient to generate the recommendations for new-users / learners. Therefore, the proposed work hybridized multi users collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental results of proposed Goal-based framework are tackled by using famous `MovieLens´ dataset while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of goal-based recommendation framework. Experimental results (precision mean: 76.284% and recall mean: 82.413%) show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.