  With the widespread use of mobile phones, users can share their location and
activity anytime, anywhere, as a form of check in data. These data reflect user
features. Long term stable, and a set of user shared features can be abstracted
as user roles. The role is closely related to the user's social background,
occupation, and living habits. This study provides four main contributions.
Firstly, user feature models from different views for each user are constructed
from the analysis of check in data. Secondly, K Means algorithm is used to
discover user roles from user features. Thirdly, a reinforcement learning
algorithm is proposed to strengthen the clustering effect of user roles and
improve the stability of the clustering result. Finally, experiments are used
to verify the validity of the method, the results of which show the
effectiveness of the method.
