Spatiotemporal Representation Learning for Translation-Based POI Recommendation
Spatiotemporal Representation Learning for Translation-Based POI Recommendation is a scholarly work, published in 2019. The main subjects of the publication include focus, recommender system, graph, graph neural network, data mining, embedding, computer science, artificial intelligence, biological robustness, information retrieval, point of interest, machine learning, exploit, and glossary of archaeology. The authors further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs.