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With the widespread use of mobile phones, users can share their location andactivity anytime, anywhere, as a form of check in data. These data reflect userfeatures. Long term stable, and a set of user shared features can be abstractedas 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 constructedfrom the analysis of check in data. Secondly, K Means algorithm is used todiscover user roles from user features. Thirdly, a reinforcement learningalgorithm is proposed to strengthen the clustering effect of user roles andimprove the stability of the clustering result. Finally, experiments are usedto verify the validity of the method, the results of which show theeffectiveness of the method.

User Role Discovery and Optimization Method based on K-means + Reinforcement learning in Mobile Applications