  Location recommendation is defined as to recommend locations (POIs) to users
in location-based services. The existing data-driving approaches of location
recommendation suffer from the limitation of the implicit modeling of the
geographical factor, which may lead to sub-optimal recommendation results. In
this work, we address this problem by introducing knowledge-driven solutions.
Specifically, we first construct the Urban Knowledge Graph (UrbanKG) with
geographical information and functional information of POIs. On the other side,
there exist a fact that the geographical factor not only characterizes POIs but
also affects user-POI interactions. To address it, we propose a novel method
named UKGC. We first conduct information propagation on two sub-graphs to learn
the representations of POIs and users. We then fuse two parts of
representations by counterfactual learning for the final prediction. Extensive
experiments on two real-world datasets verify that our method can outperform
the state-of-the-art methods.
