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Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

Tianhao Huang, Xuan Pan, Xiangrui Cai, Ying Zhang, Xiaojie Yuan

TL;DR

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.

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.

Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

TL;DR

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.

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.
Paper Structure (20 sections, 7 equations, 6 figures, 3 tables)

This paper contains 20 sections, 7 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. Method
  5. Mobility Tree Construction
  6. Node Initialization
  7. Node Information Interaction
  8. Multitask Learning
  9. Experiments
  10. Experimental Setup
  11. Datasets
  12. Baseline Models
  13. Evaluation Metrics
  14. Experiment Settings
  15. Results and Analysis
  16. ...and 5 more sections

Figures (6)

  • Figure 1: Mary and Peter exhibit varying preferences that evolve over distinct periods in one day. For example, during the period of "14:00$\sim$18:00", Mary prefers to go shopping in a mall or watch a movie in a cinema, whereas Peter likes to visit several bookstores.
  • Figure 2: Illustration of a Mobility Tree construction.
  • Figure 3: Network structures of Intra-hierarchy Communication (IAC) and Inter-hierarchy Communication (IRC).
  • Figure 4: Four-step node interaction.
  • Figure 5: Performance of MTNet with different period node numbers of 2, 3, 4, 6, 8, 12, 16, 20 and 24 on TKY.
  • ...and 1 more figures