  Product recommendation can be considered as a problem in data fusion--
estimation of the joint distribution between individuals, their behaviors, and
goods or services of interest. This work proposes a conditional, coupled
generative adversarial network (RecommenderGAN) that learns to produce samples
from a joint distribution between (view, buy) behaviors found in extremely
sparse implicit feedback training data. User interaction is represented by two
matrices having binary-valued elements. In each matrix, nonzero values indicate
whether a user viewed or bought a specific item in a given product category,
respectively. By encoding actions in this manner, the model is able to
represent entire, large scale product catalogs. Conversion rate statistics
computed on trained GAN output samples ranged from 1.323 to 1.763 percent.
These statistics are found to be significant in comparison to null hypothesis
testing results. The results are shown comparable to published conversion rates
aggregated across many industries and product types. Our results are
preliminary, however they suggest that the recommendations produced by the
model may provide utility for consumers and digital retailers.
