DISCOVERING THE IMPACT OF KNOWLEDGE IN RECOMMENDER SYSTEMS: A COMPARATIVE STUDY

Recommender systems engage user profiles and appropriate filtering techniques to assist users in findingmore relevant information over the large volume of information. User profiles play an important role inthe success of recommendation process since they model and represent the actual user needs. However, acomprehensive literature review of recommender systems has demonstrated no concrete study on the roleand impact of knowledge in user profiling and filtering approache. In this paper, we review the mostprominent recommender systems in the literature and examine the impression of knowledge extractedfrom different sources. We then come up with this finding that semantic information from the user contexthas substantial impact on the performance of knowledge based recommender systems. Finally, some newclues for improvement the knowledge-based profiles have been proposed.