  In this paper, we describe a method to tackle data sparsity and create
recommendations in domains with limited knowledge about user preferences. We
expand the variational autoencoder collaborative filtering from a single-domain
to a multi-domain setting. The intuition is that user-item interactions in a
source domain can augment the recommendation quality in a target domain. The
intuition can be taken to its extreme, where, in a cross-domain setup, the user
history in a source domain is enough to generate high-quality recommendations
in a target one. We thus create a Product-of-Experts (POE) architecture for
recommendations that jointly models user-item interactions across multiple
domains. The method is resilient to missing data for one or more of the
domains, which is a situation often found in real life. We present results on
two widely-used datasets - Amazon and Yelp, which support the claim that
holistic user preference knowledge leads to better recommendations.
Surprisingly, we find that in some cases, a POE recommender that does not
access the target domain user representation can surpass a strong VAE
recommender baseline trained on the target domain.
