Due to the prevalence of human activity in urban spaces, recommending ROIs (region-of-interests) to users, especially irregular ROIs, becomes an important task in location-based social networks. A fundamental problem is how to aggregate users’ preferences over POIs (point-of-interests) to infer the users’ region-level mobility patterns. The majority of existing studies ignore the users’ implicit interactions with individual POIs when addressing this issue. For example, a user check-in a region cannot provide any specific information about how the user likes this region (we call this phenomenon “ROI-level” implicitness) and which POI in this region the user is interested in (i.e., “POI-level” implicitness). Furthermore, existing studies adopt predefined strategies for region-level preference aggregation, that is, initializing the importance of different POIs with identical weights, which is insufficient to model the reality of social networks.

We emphasize two facts in this paper: (1) there simultaneously exists ROI-level and POI-level implicitness that blurs the users’ underlying preferences; and (2) individual POIs should have non-uniform weights and more importantly, the weights should vary across different users. To address these issues, we contribute a novel solution, namely GANR${}^2$ (Graph Attentive Neural Network for Region Recommendation). Specifically, to learn the user preferences over irregular ROIs, we provide a principled neural network equipped with two attention modules: the POI-level attention module, to select the informative POIs of one ROI, and the ROI-level attention module, to learn the ROI preferences. Moreover, we learn the interactions between users and ROIs under the NGCF (Neural Graph Collaborative Filtering) framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

由于城市空间中人类活动的普遍性，向用户推荐ROI（感兴趣的区域），尤其是不规则的ROI，已成为基于位置的社交网络中的一项重要任务。一个基本问题是如何汇总用户对POI（兴趣点）的偏好，以推断用户的区域级移动性模式。解决该问题时，大多数现有研究都忽略了用户与各个POI的隐式交互。例如，用户签入区域不能提供有关用户如何喜欢该区域（我们将此现象称为“ ROI级别”隐式）以及用户对该区域感兴趣的哪个POI（即“ POI”）的任何特定信息。级”隐式）。此外，现有研究采用了针对区域级别的偏好聚合的预定义策略，即

我们在本文中强调两个事实：（1）同时存在ROI级别和POI级别的隐式性，模糊了用户的基本偏好；和（2）从个人的POI应该具有非均匀的重量和更重要的是，权重应在不同的用户而异。为了解决这些问题，我们提供了一种新颖的解决方案，即GANR${}^2$（用于区域推荐的图形注意力神经网络）。具体来说，要了解用户对不规则ROI的偏好，我们提供了一个原理性的神经网络，其中配备了两个注意模块：POI级关注模块，用于选择一个ROI的信息性POI，以及ROI级关注模块，用于学习ROI首选项。此外，我们在NGCF（神经图协作过滤）框架下学习用户与ROI之间的交互。在两个真实世界的数据集上进行的大量实验证明了所提出框架的有效性。