Reinforced Imitative Graph Learning for Mobile User Profiling

Authors

Dongjie Wang, University of Central Florida
Pengyang Wang, University of Macau
Yanjie Fu, University of Central FloridaFollow
Kunpeng Liu, Portland State UniversityFollow
Hui Xiong, Rutgers University - Newark
Charles Hughes, University of Central Florida

Published In

IEEE Transactions on Knowledge and Data Engineering

Document Type

Pre-Print

Publication Date

8-2015

Subjects

Mobile User Profiling, Incremental Learning, Reinforcement Learning, Spatial Knowledge Graph

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.

Rights

© Copyright 2023 the authors

Description

This article has been accepted for publication in IEEE Transactions on Knowledge and Data Engineering. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/TKDE.2023.3270238

Locate the Document

10.1109/TKDE.2023.3270238

DOI

10.1109/TKDE.2023.3270238

Persistent Identifier

https://archives.pdx.edu/ds/psu/39826

Citation Details

Wang, D., Wang, P., Fu, Y., Liu, K., Xiong, H., & Hughes, C. E. (2023). Reinforced imitative graph learning for mobile user profiling. IEEE Transactions on Knowledge and Data Engineering.