SA-LSPL:Sequence-Aware Long- and Short- Term Preference Learning for next POI recommendation
<|reference_start|>SA-LSPL:Sequence-Aware Long- and Short- Term Preference Learning for next POI recommendation: The next Point of Interest (POI) recommendation aims to recommend the next POI for users at a specific time. As users' check-in records can be viewed as a long sequence, methods based on Recurrent Neural Networks (RNNs) have recently shown good applicability to this task. However, existing methods often struggle to fully explore the spatio-temporal correlations and dependencies at the sequence level, and don't take full consideration for various factors influencing users' preferences. To address these issues, we propose a novel approach called Sequence-Aware Long- and Short-Term Preference Learning (SA-LSPL) for next-POI recommendation. We combine various information features to effectively model users' long-term preferences. Specifically, our proposed model uses a multi-modal embedding module to embed diverse check-in details, taking into account both user's personalized preferences and social influences comprehensively. Additionally, we consider explicit spatio-temporal correlations at the sequence level and implicit sequence dependencies. Furthermore, SA-LSPL learns the spatio-temporal correlations of consecutive and non-consecutive visits in the current check-in sequence, as well as transition dependencies between categories, providing a comprehensive capture of user's short-term preferences. Extensive experiments on two real-world datasets demonstrate the superiority of SA-LSPL over state-of-the-art baseline methods.<|reference_end|>
@article{wang2024sa-lspl:sequence-aware,
    title={SA-LSPL:Sequence-Aware Long- and Short- Term Preference Learning for
  next POI recommendation},
    author={Bin Wang, Yan Zhang, Yan Ma, Yaohui Jin, Yanyan Xu},
    journal={arXiv preprint arXiv:2404.00367},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00367},
    primaryClass={cs.CY}
}