  Recommender Systems (RS) pervade many aspects of our everyday digital life.
Proposed to work at scale, state-of-the-art RS allow the modeling of thousands
of interactions and facilitate highly individualized recommendations.
Conceptually, many RS can be viewed as instances of statistical regression
models that incorporate complex feature effects and potentially non-Gaussian
outcomes. Such structured regression models, including time-aware varying
coefficients models, are, however, limited in their applicability to
categorical effects and inclusion of a large number of interactions. Here, we
propose Factorized Structured Regression (FaStR) for scalable varying
coefficient models. FaStR overcomes limitations of general regression models
for large-scale data by combining structured additive regression and
factorization approaches in a neural network-based model implementation. This
fusion provides a scalable framework for the estimation of statistical models
in previously infeasible data settings. Empirical results confirm that the
estimation of varying coefficients of our approach is on par with
state-of-the-art regression techniques, while scaling notably better and also
being competitive with other time-aware RS in terms of prediction performance.
We illustrate FaStR's performance and interpretability on a large-scale
behavioral study with smartphone user data.
