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Research Article Open Access
A huge amount of heterogeneous learning materials are generated on the web everyday with the rapid increase in the development of online learning technology. Besides, the learning resources are growing infinitely making it difficult for users to choose appropriate resources for their learning. Recommender systems, a subset of information filtering shows a great potential to help users in a personal learning environment to identify relevant and interesting items from a large number of items by suggesting actions to a user based on the preferences and ratings of other learners. The recommendation could be an online activity, running an online simulation or just a simple web resource. The technology finds its applications in a wide range of fields such as movies, music, news social tags, research articles, experts, social tags ,products, restaurants, jokes, financial services etc., This paper reviews the main paradigms of recommender systems and also the various methodologies that have been implemented to design recommender systems for personal learning environments.