e-learning experience using recommender systems

This paper presents the results obtained using a real e-learning recommender system where the collaborative filtering core has been adapted with the aim of weighting the importance of the recommendations in accordance with the users' knowledge. In this way, ratings from users with better knowledge of the given subject will have greater importance over ratings from users with less knowledge. In the same way, we validate the results obtained and we adjust, with just one parameter, the weight that should be awarded, in each specific e-learning recommender system, to the ratings of the users with the best reputation. The results obtained show a notable improvement regarding traditional collaborative filtering methods and suggest balanced weightings between the importance assigned to users with more or less knowledge.

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