Recommender Systems (RSs) are termed as web-based applications that make use of filtering methods and several machine learning algorithms to suggest relevant user objects. It can be said that some techniques are usually adopted or trained to develop these systems that generate lists of suitable recommendations. Conventionally, RS uses a single rating approach to preference user recommendation over an item. Recently, multi-criteria technique has been identified as a new approach of recommending user items based on several attributes or features of user items. This new technique of item recommendation has been adopted to solve several recommendation problems compared to the single rating approach. Furthermore, the predictive performance of the multi-criteria technique when tested proves to be further efficient as compared to the traditional single ratings approach. This paper gives a comparative study between two models that are based on the features and architecture of fuzzy sets system and adaptive genetic algorithm. Genetic Algorithms (GAs) are robust and stochastic search techniques centered on natural selection and evaluation that are often applied when encountering optimization problems. Fuzzy logic (FL) on the other hand, is known for its wide application in diverse fields in science. This study aims to evaluate, analyze, and compare the predictive performance of both methods and present their results. The study has been accomplished using Yahoo! Movies dataset, and the results of the performance of each model have been presented in this paper. The results proved that both techniques have significantly enhanced the system's accuracy.