  Continuous-time event data are common in applications such as individual
behavior data, financial transactions, and medical health records. Modeling
such data can be very challenging, in particular for applications with many
different types of events, since it requires a model to predict the event types
as well as the time of occurrence. Recurrent neural networks that parameterize
time-varying intensity functions are the current state-of-the-art for
predictive modeling with such data. These models typically assume that all
event sequences come from the same data distribution. However, in many
applications event sequences are generated by different sources, or users, and
their characteristics can be very different. In this paper, we extend the broad
class of neural marked point process models to mixtures of latent embeddings,
where each mixture component models the characteristic traits of a given user.
Our approach relies on augmenting these models with a latent variable that
encodes user characteristics, represented by a mixture model over user behavior
that is trained via amortized variational inference. We evaluate our methods on
four large real-world datasets and demonstrate systematic improvements from our
approach over existing work for a variety of predictive metrics such as
log-likelihood, next event ranking, and source-of-sequence identification.
