2024/03/17 by Tianhao Huang, Huang, Tianhao, Xuan Pan +7 · 2 citations
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Data Management and Algorithms #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2403.12100
openalex publication_date 2024/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over ten state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.