Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

TL;DR

This paper introduces the Mobility Tree data structure and MTNet framework to improve next POI recommendation by capturing users' time slot-specific behavioral patterns, demonstrating superior performance over existing models.

Contribution

The paper proposes the novel Mobility Tree structure and MTNet model, enabling hierarchical and multi-granularity learning of user preferences across different time slots.

Findings

MTNet outperforms ten state-of-the-art models on real-world datasets.

Mobility Tree effectively captures time slot-specific user behaviors.

Multitask training enhances the robustness of preference representations.

Abstract

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.