Abstract
In this paper we present the architecture of a hybrid recommender system to support an adaptive hypermedia educational (AHE) system. Currently the instructor (using fuzzy rules) specifies the sequence in which learning objects are presented to students. The instructor can also give students a chance to choose from a pool of objects and helps them make their selection by assigning to each object a recommendation rating based on the student’s profile. We propose a hybrid recommender system that uses collaborative filtering techniques together with fuzzy inference systems to provide recommendations, considering the instructor’s experience as well as the ratings given by similar students.
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© 2009 Springer-Verlag Berlin Heidelberg
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García-Valdez, M., Parra, B. (2009). A Hybrid Recommender System Architecture for Learning Objects. In: Castillo, O., Pedrycz, W., Kacprzyk, J. (eds) Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control. Studies in Computational Intelligence, vol 257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04514-1_11
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DOI: https://doi.org/10.1007/978-3-642-04514-1_11
Publisher Name: Springer, Berlin, Heidelberg
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