  In recent years, there has been significant interest in understanding users'
online content consumption patterns. But, the unstructured, high-dimensional,
and dynamic nature of such data makes extracting valuable insights challenging.
Here we propose a model that combines the simplicity of matrix factorization
with the flexibility of neural networks to efficiently extract nonlinear
patterns from massive text data collections relevant to consumers' online
consumption patterns. Our model decomposes a user's content consumption journey
into nonlinear user and content factors that are used to model their dynamic
interests. This natural decomposition allows us to summarize each user's
content consumption journey with a dynamic probabilistic weighting over a set
of underlying content attributes. The model is fast to estimate, easy to
interpret and can harness external data sources as an empirical prior. These
advantages make our method well suited to the challenges posed by modern
datasets. We use our model to understand the dynamic news consumption interests
of Boston Globe readers over five years. Thorough qualitative studies,
including a crowdsourced evaluation, highlight our model's ability to
accurately identify nuanced and coherent consumption patterns. These results
are supported by our model's superior and robust predictive performance over
several competitive baseline methods.
