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

more »

... tively 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. Sparsity of User Check-in Data. One of the major challenges is to overcome the sparsity in the user check-in data. The user-POI matrix can be extremely sparse despite of millions of POIs and users in LBSNs. Temporal Reasoning. Location-based POI recommendation systems utilize the temporal context [24] for the purpose of modeling personal preferences. The temporal information reflects users' needs and choices throughout the day.