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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Recommender systems are now a popular research area and have become powerful tools to present personalized offers to users in many domains (e. g. e-commerce, e-learning). In this paper, we introduced an approach of personalization which extracts learners' preferences based on learning processes and learning activities (e. g. writing summary) and provides more relevant, personalized recommendations. Keyword maps proposed with keywords and various relations among them in this article describe content of each learning object and knowledge of each learner existing. The research hypothesizes that keyword maps should help to increase both the relevance and complement of learning materials recommendation. Thereafter, learners' comprehension degree and proficiency level are inferred by these keyword maps. According to the learners' comprehension degree and learners' proficiency levels, the system filters out the irrelevant learning processes and recommends the learning materials separately.