With the prevalence of mobile devices and the gradually severe information overloading in big data era, how to accurately recommend apps to users based on their interests and their user behaviours becomes an important problem. Most existing recommender systems suffer from common weaknesses such as making recommendations that are too dull and lacking diversity, and they also rarely consider the user behaviours when making recommendations, resulting in sub-optimal recommendation performance. In this paper, we propose a multi-dimensional app recommender system that takes both the hierarchically clustered user features and user behaviours into account when making recommendations. After being processed by singular value decomposition (SVD) that performs denoising and dimensionality reduction, the user features are then clustered into several user groups by the hierarchical clustering. The user behaviour information is subsequently leveraged together with the user features and inputted into the Bayesian model to produce an app recommendation list in the descending order of the probability of being accurate. Hence, the accuracy of the app recommendation is improved. This paper finally implements the proposed recommender system under a distributed framework, which ensures the efficiency and the reliability of the system. The experiments demonstrate an average within-group deviation of 0.4 after the SVD is applied. The recall of the app recommender system after the hierarchical clustering raises to 73%, which shows a 5%～16% performance boost compared with collaborative-filtering-based and association-rule-based methods. All evaluation metrics of the Bayesian model also presents a performance gain of around 10%, which verifies the effectiveness of the proposed recommender system.