Abstract
Currently, information overload can make selecting the information appropriately as time consuming needs. Therefore created a recommender system that helps the selection of information appropriately and personalized as needed. A system recommender has various types and support techniques to determine recommendations, including ontology and machine learning. Ontology is a conceptualization of the representation of knowledge that can be translated into machine language as well as machine learning which is the formalization of human learning applied to the computer in order to gain knowledge from the real world is considered as techniques that can help find the right recommendations. This study conducted of 750 previous studies that have been carefully analyzed using the Kitchenham method to find the most commonly used recommender system and the roles of both techniques in the system recommendations.
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