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In recent years, point-of-interest (POI) recommendation has been extensively studied, with existing methods typically modeling user preferences through the integration of multi-factor information (e.g., temporal, spatial, and categorical features) and capturing the periodicity and discontinuity of user check-in sequences. However, these approaches struggle with data sparsity, missing data, and noisy data, leading to suboptimal user representations. To effectively mitigate the above problems, we utilize contrastive self-supervised learning techniques to achieve data augmentation and apply them to the next POI recommendation task. Specifically, we propose DACL (Data Augmentation through Contrastive Self-supervised Learning), a novel framework that unifies next POI recommendation and contrastive self-supervised learning (SSL) via a multi-task strategy. Furthermore, DACL introduces five tailored data augmentation operations to generate high-quality contrastive views, mitigating data limitations while enhancing robustness. Extensive experiments on two real-world datasets (NYC and TKY) demonstrate that DACL significantly outperforms state-of-the-art baselines, achieving \(14.3\%\) and \(4.3\%\) improvements in Recall@10 and NDCG@5, respectively, while maintaining superior robustness against noisy and sparse scenarios.

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Leveraging Data Augmentation Through Contrastive Self-supervised Learning for Next Point-of-Interest Recommendation

  • 会议论文

  • 发表日期：

    2026年

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  • Limin Guo,
  • Weijia Liu,
  • Zhi Cai,
  • Xing Su

摘要

In recent years, point-of-interest (POI) recommendation has been extensively studied, with existing methods typically modeling user preferences through the integration of multi-factor information (e.g., temporal, spatial, and categorical features) and capturing the periodicity and discontinuity of user check-in sequences. However, these approaches struggle with data sparsity, missing data, and noisy data, leading to suboptimal user representations. To effectively mitigate the above problems, we utilize contrastive self-supervised learning techniques to achieve data augmentation and apply them to the next POI recommendation task. Specifically, we propose DACL (Data Augmentation through Contrastive Self-supervised Learning), a novel framework that unifies next POI recommendation and contrastive self-supervised learning (SSL) via a multi-task strategy. Furthermore, DACL introduces five tailored data augmentation operations to generate high-quality contrastive views, mitigating data limitations while enhancing robustness. Extensive experiments on two real-world datasets (NYC and TKY) demonstrate that DACL significantly outperforms state-of-the-art baselines, achieving \(14.3\%\) and \(4.3\%\) improvements in Recall@10 and NDCG@5, respectively, while maintaining superior robustness against noisy and sparse scenarios.