Next (or successive) point-of-interest (POI) recommendation, which aims to predict where users are likely to go next, has recently emerged as a new research focus of POI recommendation. Most of the previous studies on next POI recommendation attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, few of the next POI recommendation approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. We also carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely, Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is highly efficient among six neural-network-based methods, four of which utilize the attention mechanism.

中文翻译：

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单信噪比

下一个（或连续的）兴趣点（POI）推荐，旨在预测用户下一步可能去哪里，最近成为 POI 推荐的新研究焦点。先前关于下一个 POI 推荐的研究大多试图将用户签到的时空信息和顺序模式结合到推荐模型中，以预测目标用户的下一步行动。然而，接下来的 POI 推荐方法很少利用每个用户朋友的社会影响力。在这项研究中，我们讨论了下一个 POI 推荐的新主题，并提出了一个深度注意力网络，用于社交感知下一个 POI 推荐，称为 DAN-SNR。特别是，DAN-SNR 利用自注意力机制而不是递归神经网络的架构，以统一的方式对顺序影响和社会影响进行建模。此外，我们设计并实现了两个平行的渠道来分别捕捉短期用户偏好和长期用户偏好以及社会影响力。通过利用多头自注意力，DAN-SNR 可以有效地模拟任意两个历史签到之间的远程依赖关系，并自适应地权衡它们对下一个目的地的贡献。我们还使用从两个流行的基于位置的社交网络 Gowalla 和 Brightkite 收集的大规模真实世界数据集进行了综合评估。