hybrid adaptive educational hypermedia ‎recommender accommodating user’s learning ‎style and web page features‎

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 coldstart 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 graysheep 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 featurebased 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 feldersilverman 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.‎