  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.
