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A Framework for User Profile Enrichment in Content-based Recommender Systems by inferring the Semantic Couplings

Content-based recommender systems predict the inclination of users towards content-based items such as documents, news articles, web-pages, journals, etc. according to their interest area. In this work, a framework for the enrichment of user profiles in content-based recommender systems has been proposed. The enrichment of user profiles takes place from three different sources: the similar users existing in the system, the documents referred by the users and thirdly the semantic relationship between the features of the user profile. This framework also addresses the cold-start problem associated with new users in recommender systems. After Experimentation, improved recall and precision have been observed after the iterations.