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Syst."],"published-print":{"date-parts":[[2018,10,31]]},"abstract":"<jats:p>Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users on Location-Based Social Networks (LBSNs). In this article, we propose a probabilistic model to find the mapping between user-annotated tags and locations\u2019 taste keywords. Furthermore, we introduce a dataset on locations\u2019 contextual appropriateness and demonstrate its usefulness in predicting the contextual relevance of locations. We investigate four approaches to use our proposed mapping for addressing the data sparsity problem: one model to reduce the dimensionality of location taste keywords and three models to predict user tags for a new location. Moreover, we present different scores calculated from multiple LBSNs and show how we incorporate new information from the mapping into a POI recommendation approach. Then, the computed scores are integrated using learning to rank techniques. 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