Argumentation-based hybrid recommender system for recommending learning objects

Abstract

Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences. In virtual learning environments, Educational Recommender Systems deliver learning objects according to the student’s characteristics, preferences and learning needs. A learning object is an educational content unit, which once found and retrieved may assist students in their learning process. In previous work, authors have designed and evaluated several recommendation techniques for delivering the most appropriate learning object for each specific student. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques. However, traditional hybidization methods fail when the learning objects delivered by each recommendation technique are very different from those selected by the other techniques (there is no agreement about the best learning object to recommend). In this paper, we present a hybrid recommendation method based on argumentation theory that combines content-based, collaborative and knowledge-based recommendation techniques and provides the students with those objects for which the system is able to generate more arguments to justify their suitability. This method has been tested by using a database with real data about students and learning objects, getting promising results.