Giving or recommending appropriate content based on the quality of experience is the most important in recommender systems. This study proposes a new CBCF (Clustering-Based CF) method using an Incentivized/Penalized User (IPU) model, which is thus easy to implement. The purpose of this study is to improve recommendation performance of accuracy, recall and F1-score by studying the differences of users’ preferences through IPU model. This study formulates a constrained optimization problem in which we aim to maximize the recall (or equivalently F1-score) for a given precision. To this end, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient. Afterward, we give each item an incentive/penalty according to the preference tendency by users within the same cluster. Experiments show that under the condition of given accuracy, the recall rate of the proposed algorithm can be improved by about 50%.