DAN-SNR：一个用于社会感知下一个兴趣点推荐的深度关注网络
DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation
摘要：
下一个（或连续的）兴趣点（POI）推荐近年来引起了越来越多的关注。以往的研究大多试图将时空信息和用户签到的序列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，这些方法都没有利用每个用户的朋友的社会影响力。在这项研究中，我们讨论了一个新的话题下一个兴趣点推荐，并提出了一个深度关注网络的社会意识下一个兴趣点推荐称为DAN-SNR。特别地，DAN-SNR利用自我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并行的渠道，以捕捉短期用户偏好和长期用户偏好以及社会影响力，分别。通过利用多头自注意，DAN-SNR可以有效地建模任何两个历史签入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。此外，我们使用从两个流行的基于位置的社交网络，即Gowalla和Rekite收集的大规模真实世界数据集进行了全面评估。实验结果表明，DAN-SNR优于7竞争基准方法的推荐性能，是高效率的六个神经网络和注意力为基础的方法。
英文摘要：
Next (or successive) point-of-interest (POI) recommendation has attracted increasing attention in recent years. Most of the previous studies 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, none of these 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. Also, we 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 of high efficiency among six neural-network- and attention-based 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。
