SA-LSPL:Sequence-Aware Long- and Short- Term Preference Learning for next POI recommendation

• URL: http://arxiv.org/abs/2404.00367v1
• Date: Sat, 30 Mar 2024 13:40:25 GMT
• Title: SA-LSPL:Sequence-Aware Long- and Short- Term Preference Learning for next POI recommendation
• Authors: Bin Wang, Yan Zhang, Yan Ma, Yaohui Jin, Yanyan Xu,
• Abstract summary: Point of Interest (POI) recommendation aims to recommend the POI for users at a specific time. We propose a novel approach called Sequence-Aware Long- and Short-Term Preference Learning (SA-LSPL) for next-POI recommendation.
• Score: 19.40796508546581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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