Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation
DOI:
https://doi.org/10.1609/aaai.v38i8.28697Keywords:
DMKM: Recommender Systems, DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data, ML: Learning Preferences or RankingsAbstract
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 eleven 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.Downloads
Published
2024-03-24
How to Cite
Huang, T., Pan, X., Cai, X., Zhang, Y., & Yuan, X. (2024). Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 8535-8543. https://doi.org/10.1609/aaai.v38i8.28697
Issue
Section
AAAI Technical Track on Data Mining & Knowledge Management