Enhanced Collaborative Filtering to Recommender Systems of Technology Enhanced Learning
Majda Maâtallah, Hassina Seridi · 2012
Abstract. Recommender Systems (RSs) are largely used nowadays in many areas to generate items of interest to users. Recently, they are applied in the Technology Enhanced Learning (TEL) field to let recommending relevant learning resources to support teachers or learners ’ need. In this paper we propose a novel recommendation technique that combines a fuzzy collaborative filtering algorithm with content based one to make better recommendation, using learners ’ preferences and importance of knowledge to recommend items with different context in order to alleviate the Stability vs. Plasticity problem of TEL Recommender Systems. Empirical evaluations show that the proposed technique is feasible and effective.