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The rapid development of internet technologies in recent decades has imposed a heavy information burden on users. The popularity of recommender systems has evolved to provide suggestions and recommendations to the user for relevant information from web according to their preferences. Most recommender systems use collaborative-filtering or content based methods to predict new items of interest for users. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. In this paper, the authors propose a standard architectural framework Semantic Enhanced Personalizer (SEP) which integrates three recommendation techniques i.e., original, semantic and category based.
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