Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods.

中文翻译：

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带有提示的空间对象建议：当空间粒度很重要时

现有的空间对象推荐算法通常在对对象进行排名时一视同仁。但是，空间对象通常覆盖不同级别的空间粒度，因此是异构的。例如，一个用户可能更喜欢被推荐为一个区域（例如曼哈顿），而另一用户可能更喜欢一个场所（例如餐馆）。即使对于同一用户，偏好也会在数据探索的不同阶段发生变化。在本文中，我们研究如何在不同的空间粒度级别上支持top-k空间对象建议，以使不同粒度的空间对象（例如城市，郊区或建筑物）成为关注点（POI）。为了解决此问题，我们建议使用POI树，该树可捕获POI之间的空间包含关系。我们设计了一个新颖的多任务学习模型，称为MPR（Multi-level POI Recommendation的缩写），其中每个任务旨在以特定的空间粒度级别返回前k个POI。每个任务包括两个子任务：（i）基于属性的表示学习；（ii）基于交互的表示学习。第一个子任务学习用户和POI的特征表示，直接从其配置文件中捕获属性。第二个子任务将用户-POI交互合并到模型中。此外，MPR可以基于三种类型的提示：为什么要向用户提出某些建议：用户方面，POI方面和交互方面。我们使用两个真实的数据集以经验方式验证了我们的方法，并显示了在几种最先进方法上有望实现的性能改进。