Document Type: Research/Original/Regular Article

Authors

1 Department of Computer Engineering, Yazd University and University of Qom, Alghadir Blvd., Qom, Iran.

2 Department of Computer Engineering and IT, University of Qom, Alghadir Blvd., Qom, Iran

3 Department of Computer Engineering, Azad University of Kashan, Kashan, Iran. ‎

Abstract

Personalized recommenders have proved to be of use as a solution to reduce the information overload ‎problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers ‎suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. ‎Furthermore, obtaining learner’s preferences is cumbersome. Most studies have only focused on similarity ‎between the interest profile of a user and those of others. However, it can lead to the gray-sheep problem, ‎in which users with consistently different opinions from the group do not benefit from this approach. On ‎this basis, matching the learner’s learning style with the web page features and mining specific attributes ‎is more desirable. The primary contribution of this research is to introduce a feature-based recommender ‎system that delivers educational web pages according to the user's individual learning style. We propose an ‎Educational Resource recommender system which interacts with the users based on their learning style ‎and cognitive traits. The learning style determination is based on Felder-Silverman theory. Furthermore, ‎we incorporate all explicit/implicit data features of a page and the elements contained in them that have an ‎influence on the quality of recommendation and help the system make more effective recommendations.‎

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