Reinforced Imitative Graph Learning for Mobile User Profiling

TL;DR

This paper introduces a reinforcement learning framework for mobile user profiling that uses imitation learning, a spatial knowledge graph, and a mutual-updating strategy to accurately predict user mobility patterns.

Contribution

It presents a novel reinforcement imitative graph learning framework incorporating a spatial knowledge graph and mutual-updating strategy for dynamic user profiling.

Findings

Outperforms existing methods in mobility prediction accuracy.

Effectively captures dynamic user behavior over time.

Demonstrates the benefit of using a knowledge graph in user profiling.

Abstract

Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users' mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.