Improving Location Recommendation with Urban Knowledge Graph
This work addresses location recommendation for users in location-based services, representing an incremental improvement by incorporating explicit geographical and functional information.
The paper tackles the problem of suboptimal location recommendation due to implicit modeling of geographical factors by introducing a knowledge-driven solution using an Urban Knowledge Graph (UrbanKG) and a novel method UKGC, which outperforms state-of-the-art methods in experiments on two real-world datasets.
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.