User Role Discovery and Optimization Method based on K-means + Reinforcement learning in Mobile Applications
This work addresses user role identification for mobile application developers, but it appears incremental as it combines existing methods (K-means and reinforcement learning) without introducing a fundamentally new approach.
The study tackled the problem of discovering user roles from mobile check-in data by constructing user feature models, using K-means for role discovery, and applying reinforcement learning to enhance clustering stability, with experiments verifying the method's effectiveness.
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.