In recent studies on recommender models, association rules have been applied in many studies to improve the effectiveness of recommender models. However, these studies also reveal some drawbacks, such as the models take a considerable amount of time to generate association rules for large datasets; generation algorithms can ignore rules with the significant implication that affect the quality of recommender models. This study proposes collaborative filtering recommender models (CF models) based on association rules following an asymmetric approach of the statistical implicative analysis method to enhance the precision of recommender models. Through experiments on standard datasets and quality comparison with other CF models, we conclude that the proposed models based on the asymmetric relationship achieve better accuracy on the experimental datasets.