In this paper, we present a hybrid recommendation approach for discovering potential preferences of individual users. The proposed approach provides a flexible solution that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. This facilitates to obtain user clusters which have diverse preference from multi-view for improving effectiveness and diversity of recommendation. The presented algorithm works in three phases: data preprocessing and multidimensional clustering, choosing the appropriate clusters and recommending for the target user. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach is likely to trade-off on increasing the diversity of recommendations while maintaining the accuracy of recommendations.