Abstract:

In E-learning, in order to satisfy users' personalized demands for learning resources, in this paper we present a learning resources personalized recommendation algorithm based on semantic web technology. First of all, based on users' evaluations and browsing behaviors of the learning resources, we define the learning resource set and core concept set for a user; secondly, based on the relations of concepts in domain ontology, we obtain the user's preference similarity by computing semantic similarity between the different users' learning resource sets and core concept sets respectively. At last, we recommend collaboratively the learning resources according to the user's preference similarity. In addition, the learning objects for management of learning resources can reduce the complexity of similarity computation. We applied this algorithm to the personalized learning resources recommendation system, the experiments show that the algorithm improved the effectiveness of the recommended learning resources, especially for new resources and new registered users.

Key words: computer application, learning resource, semantic similarity, user preference, semantic Web