Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an I nteraction-enhanced and T ime-aware G raph C onvolution N etwork (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods.

中文翻译：

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用于旅游企业连续兴趣点推荐的交互增强和时间感知图卷积网络

通过物联网 (IoT) 设备收集包含用户位置偏好的大量用户签到数据，包括基于位置的社交网络中的手机和其他传感设备。它可以帮助旅游企业智能预测用户的兴趣和喜好，为他们提供科学的旅游路径，增加企业收入。因此，连续兴趣点（POI）推荐已成为增强物联网（AIoT）的热门研究课题。目前，已将各种方法应用于连续的 POI 推荐。其中，基于递归神经网络的方法致力于挖掘 POI 之间的序列关系，但忽略了用户与 POI 之间的高阶关系。基于图神经网络的方法可以捕获高阶连通性，但它没有考虑 POI 的动态及时性。因此，我们提出一个交互增强和时间感知图表卷积用于连续 POI 推荐的网络 (ITGCN)。具体来说，我们设计了一个改进的图卷积网络，用于学习用户和 POI 的动态表示。我们还设计了一个自注意力聚合器，以选择性地将高阶连接嵌入到节点表示中。企业管理系统可以预测用户的喜好，有助于未来的规划和发展。最后，实验结果证明，与现有方法相比，ITGCN 带来了更好的结果。