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Accounts are often shared by multiple users, each of them having different item consumption and temporal habits. Identifying of the active user can lead to improvements in a variety of services by switching from account personalized services to user personalized services. To do so, we develop a topic model extending the Latent Dirichlet Allocation using a hidden variable representing the active user and assuming consumption times to be generated by latent time topics. We create a new dataset of composite accounts from real users to test the identification capabilities of our model. We show that our model is able to learn temporal patterns from the whole set of accounts and infer the active user using both the consumption time and the consumed item.
