Discrete time event sequences from web users are commonly encountered
in a variety of real-world domains, from digital marketing
to recommender systems. Most of the literature in the area focuses
on supervised prediction tasks of the user’s next action or return
time. However, little attention has been paid to the challenging task
of modeling user evolution and behavior in a quantitative fashion.
For example, progress of the users’ knowledge in an online tutorial,
user exploration, or engagement. We propose methods to perform
a time-varying clustering along with event predictions in a unified
domain agnostic framework. Our framework can help track
the evolution of the users as they interact with the platform. We
evaluate our methods on three real-world datasets and show that
our method performs at par with the supervised baseline on the
prediction tasks while providing meaningful clusters.