Role mining is to define a role set to implement the role-based access control (RBAC) system and regarded as one of the most important and costliest implementation phases. While various role mining models have been proposed, we find that user experience/perception – one ultimate goal for any information system – is surprisingly ignored by the existing works. One advantage of RBAC is to support multiple role assignments and allow a user to activate the necessary role to perform the tasks at each session. However, frequent role activating and deactivating can be a tendinous thing from the user perspective. A user-friendly RBAC system is expected to assign few roles to every user. So in this paper we propose to incorporate to the role mining process a user-role assignment constraint that mandates the maximum number of roles each user can have. Under this rationale, we formulate user-oriented role mining as the user role mining problem, where all users have the same maximal role assignments, the personalized role mining problem, where users can have different maximal role assignments, and the approximate versions of the two problems, which tolerate a certain amount of deviation from the complete reconstruction. The extra constraint on the maximal role assignments poses a great challenge to role mining, which in general is already a hard problem. We examine some typical existing role mining methods to see their applicability to our problems. In light of their insufficiency, we present a new algorithm, which is based on a novel dynamic candidate role generation strategy, tailored to our problems. Experiments on benchmark data sets demonstrate the effectiveness of our proposed algorithm.