  Point-of-Interest (POI) recommendation is one of the most important
location-based services helping people discover interesting venues or services.
However, the extreme user-POI matrix sparsity and the varying spatio-temporal
context pose challenges for POI systems, which affects the quality of POI
recommendations. To this end, we propose a translation-based relation embedding
for POI recommendation. Our approach encodes the temporal and geographic
information, as well as semantic contents effectively in a low-dimensional
relation space by using Knowledge Graph Embedding techniques. To further
alleviate the issue of user-POI matrix sparsity, a combined matrix
factorization framework is built on a user-POI graph to enhance the inference
of dynamic personal interests by exploiting the side-information. Experiments
on two real-world datasets demonstrate the effectiveness of our proposed model.
