  An important metric of users' satisfaction and engagement within on-line
streaming services is the user session length, i.e. the amount of time they
spend on a service continuously without interruption. Being able to predict
this value directly benefits the recommendation and ad pacing contexts in music
and video streaming services. Recent research has shown that predicting the
exact amount of time spent is highly nontrivial due to many external factors
for which a user can end a session, and the lack of predictive covariates. Most
of the other related literature on duration based user engagement has focused
on dwell time for websites, for search and display ads, mainly for post-click
satisfaction prediction or ad ranking.
  In this work we present a novel framework inspired by hierarchical Bayesian
modeling to predict, at the moment of login, the amount of time a user will
spend in the streaming service. The time spent by a user on a platform depends
upon user-specific latent variables which are learned via hierarchical
shrinkage. Our framework enjoys theoretical guarantees and naturally
incorporates flexible parametric/nonparametric models on the covariates,
including models robust to outliers. Our proposal is found to outperform
state-of- the-art estimators in terms of efficiency and predictive performance
on real world public and private datasets.
