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

TL;DR

This paper introduces a novel method combining K-means clustering and reinforcement learning to discover and optimize user roles based on check-in data from mobile applications, enhancing clustering stability and validity.

Contribution

It proposes a new approach integrating user feature modeling, K-means clustering, and reinforcement learning to improve user role discovery in mobile app data.

Findings

Effective user role discovery from check-in data

Enhanced clustering stability through reinforcement learning

Validated method with experimental results

Abstract

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.