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<p>Next Point-of-Interest (POI) recommendation predicts a user’s next likely destination based on historical check-ins, enhancing trip planning and location discovery. Current models, including sequence-based and graph-based approaches, often lack adaptability to temporal variations in relationships and treat graph construction as an isolated pre-training step, limiting their effectiveness. To address these challenges, we propose the Time-aware Graph Flashback Network (TGFN), introducing a Spatial-Temporal Knowledge Graph (STKG) that captures dynamic, time-evolving POI relationships. Our Time-TransH model learns both temporal edge variations and core feature representations, enabling real-time updates through weighted convolutions on location nodes and neighbors. By integrating relationship learning in an end-to-end framework, TGFN ensures accurate node representations. Experiments on real-world datasets show that TGFN significantly outperforms existing methods, achieving higher accuracy across multiple metrics.</p>

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Time-aware graph flashback network for next location recommendation

  • 期刊论文

  • 发表日期：

    2025年5月24日

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  • Junheng Gao,
  • Wei Liu,
  • Shangsong Liang

摘要

Next Point-of-Interest (POI) recommendation predicts a user’s next likely destination based on historical check-ins, enhancing trip planning and location discovery. Current models, including sequence-based and graph-based approaches, often lack adaptability to temporal variations in relationships and treat graph construction as an isolated pre-training step, limiting their effectiveness. To address these challenges, we propose the Time-aware Graph Flashback Network (TGFN), introducing a Spatial-Temporal Knowledge Graph (STKG) that captures dynamic, time-evolving POI relationships. Our Time-TransH model learns both temporal edge variations and core feature representations, enabling real-time updates through weighted convolutions on location nodes and neighbors. By integrating relationship learning in an end-to-end framework, TGFN ensures accurate node representations. Experiments on real-world datasets show that TGFN significantly outperforms existing methods, achieving higher accuracy across multiple metrics.