User-Dependent Neural Sequence Models for Continuous-Time Event Data
This work addresses the problem of personalized event prediction in applications like behavior tracking and healthcare, offering an incremental improvement over existing neural methods by incorporating user variability.
The paper tackled the challenge of modeling continuous-time event data from multiple users by extending neural marked point process models with latent mixture embeddings to capture user-specific traits, resulting in systematic improvements in predictive metrics like log-likelihood and next event ranking across four real-world datasets.
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.