SA-LSPL：面向下一个兴趣点推荐的序列感知长短期偏好学习
SA-LSPL:Sequence-Aware Long- and Short- Term Preference Learning for next POI recommendation
AI总结：
本文提出SA-LSPL模型，通过多模态嵌入、序列级时空相关性与类别转移依赖建模，同时捕获用户长短期偏好，并在两个真实数据集上验证其优于现有基线方法。
摘要：
下一个兴趣点（POI）推荐旨在在特定时间为用户推荐下一个POI。由于用户的签到记录可以被视为一个长序列，基于循环神经网络（RNNs）的方法近年来在该任务上表现出良好的适用性。然而，现有方法往往难以充分挖掘序列层面的时空相关性和依赖关系，并且没有充分考虑影响用户偏好的各种因素。为了解决这些问题，我们提出了一种新颖的方法，称为序列感知长短期偏好学习（SA-LSPL），用于下一个POI推荐。我们结合多种信息特征来有效建模用户的长期偏好。具体而言，我们提出的模型使用多模态嵌入模块来嵌入多样化的签到细节，同时全面考虑用户的个性化偏好和社交影响。此外，我们考虑了序列层面的显式时空相关性以及隐式序列依赖关系。进一步地，SA-LSPL学习当前签到序列中连续和非连续访问之间的时空相关性，以及类别之间的转移依赖关系，从而全面捕获用户的短期偏好。在两个真实世界数据集上的大量实验表明，SA-LSPL优于最先进的基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
