Relation Embedding for Personalised POI Recommendation

Point-of-interest (POI) recommendation is one of the most important location-based services to help people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatial-temporal context create challenges for POI recommendation, which affects the performance of POI recommendation quality. 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 content information effectively in a low-dimension relation space by using knowledge graph embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a fused 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.