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Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,6,28]]},"abstract":"<jats:p>Both app developers and service providers have strong motivations to understand<jats:italic>when<\/jats:italic>and<jats:italic>where<\/jats:italic>certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture<jats:italic>when<\/jats:italic>,<jats:italic>where<\/jats:italic>, and<jats:italic>what<\/jats:italic>semantics of apps are used to predict the future usage. 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