[2505.19965] Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction

Computer Science > Artificial Intelligence arXiv:2505.19965 (cs) [Submitted on 26 May 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction Authors:Yu Wang, Junshu Dai, Yuchen Ying, Hanyang Yuan, Zunlei Feng, Tongya Zheng, Mingli Song View a PDF of the paper titled Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction, by Yu Wang and 6 other authors View PDF HTML (experimental) Abstract:Human mobility prediction is crucial for applications ranging from location-based recommendations to urban planning, which aims to forecast users' next location visits based on historical trajectories. While existing mobility prediction models excel at capturing sequential patterns through diverse architectures for different scenarios, they are hindered by the long-tailed distribution of location visits, leading to biased predictions and limited applicability. This highlights the need for a solution that enhances the long-tailed prediction capabilities of these models with broad compatibility and efficiency across diverse architectures. To address this need, we propose the first architecture-agnostic plugin for long-tailed human mobility prediction, named \textbf{A}daptive \textbf{LO}cation \textbf{H}ier\textbf{A}rchy learning (ALOHA). Inspired by Maslow's theory of human motivation, we exploit and explore common mobility knowledge of head and tail locations derived from human mobility traject...