Recommender systems are used to analyze users' preferences through their past activities and to personalize recommendations for each user based on what they might be interested in. The performance of the recommender system is most commonly measured using only recommendation accuracy. However, recommending accurate items does not mean that the generated recommendation is the best for the user because it can be biased towards some items that have a higher chance of being liked by users, such as popular items. Recommendations become repetitive and obvious with biased item selection and are less likely to be personalized. To mitigate bias and repetitiveness, recommendation diversity has been studied. However, diversity has a trade-off relationship with accuracy. Modifying the recommendation algorithm to consider diversity while learning about user preferences would not only cause loss in accuracy, but also lead to less precise reading of user preferences. Instead, using ranking methods to re-rank the priority of items predicted, the recommendation algorithm would keep the preciseness of the algorithm. In this study, a ranking method using the appearance frequency of items to restrict the items from being frequently recommended will be proposed. The experimental results showed that the proposed method consistently improved diversity in multiple diversity metrics.