Spatiotemporal Representation Learning for Translation-Based POI Recommendation

<jats:p> The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user’s decision-making for choosing a POI to visit. In this article, we focus on the <jats:italic>spatiotemporal context-aware</jats:italic> POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a <jats:italic>spatiotemporal context-aware</jats:italic> and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a “transition space” where spatiotemporal contexts (i.e., a < <jats:italic>time, location</jats:italic> > pair) are modeled as <jats:italic>translation vectors</jats:italic> operating on users and POIs. We 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. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem. </jats:p>