  Recommender system research suffers from a disconnect between the size of
academic data sets and the scale of industrial production systems. In order to
bridge that gap, we propose to generate large-scale user/item interaction data
sets by expanding pre-existing public data sets. Our key contribution is a
technique that expands user/item incidence matrices matrices to large numbers
of rows (users), columns (items), and non-zero values (interactions). The
proposed method adapts Kronecker Graph Theory to preserve key higher order
statistical properties such as the fat-tailed distribution of user engagements,
item popularity, and singular value spectra of user/item interaction matrices.
Preserving such properties is key to building large realistic synthetic data
sets which in turn can be employed reliably to benchmark recommender systems
and the systems employed to train them. We further apply our stochastic
expansion algorithm to the binarized MovieLens 20M data set, which comprises
20M interactions between 27K movies and 138K users. The resulting expanded data
set has 1.2B ratings, 2.2M users, and 855K items, which can be scaled up or
down.
