With the emergence of personality advancements in the field of science and research, recommendation systems are becoming an important and integral part of our daily life. A recommender system is a software application that examines user actions and preferences to provide recommendations for items that align with the user's potential interests. The aim of a recommender system is to offer customized suggestions that enhance the user's experience and satisfaction. Personality-centric recommender systems fall within the realm of recommendation systems that employ information regarding a user's individuality attributes to produce customized suggestions. These recommender systems have the ability to effectively tackle traditional challenges like the cold start problem and sparsity issues. The primary goal of this paper is to systematically conclude the methodologies employed in recommender systems and explore significant applications of personality-aware recommender systems. Personality computing is discussed in which big five model is described in more detail. Deep learning techniques are also included which defines the state of art of recommender system.