  We consider a particular instance of a common problem in recommender systems:
using a database of book reviews to inform user-targeted recommendations. In
our dataset, books are categorized into genres and sub-genres. To exploit this
nested taxonomy, we use a hierarchical model that enables information pooling
across across similar items at many levels within the genre hierarchy. The main
challenge in deploying this model is computational: the data sizes are large,
and fitting the model at scale using off-the-shelf maximum likelihood
procedures is prohibitive. To get around this computational bottleneck, we
extend a moment-based fitting procedure proposed for fitting single-level
hierarchical models to the general case of arbitrarily deep hierarchies. This
extension is an order of magnetite faster than standard maximum likelihood
procedures. The fitting method can be deployed beyond recommender systems to
general contexts with deeply-nested hierarchical generalized linear mixed
models.
