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With proliferation of learning contents on the web, finding suitable ones has become a very difficult and complicated task for online learners, to achieve better performance. Nevertheless, recommender systems can be a solution to the problem. However, recommendation systems haven't been sufficiently used in e-learning, in comparison with other fields (i.e. commerce, medicine and so on). In this paper, we propose a semantic recommender system for e-learning by means of which, learners will be able to find and choose the right learning materials suitable to their field of interest. The proposed web based recommendation system comprises ontology and web ontology language (OWL) rules. Rule filtering will be used as recommendation technique. Our proposed recommendation system architecture consists of two subsystems; Semantic Based System and Rule Based System. Modules for either subsystem are; Observer, Learner profile, Recommendation storage and User interface.