  In this paper we describe a method to tackle data sparsity and create
recommendations in domains with limited knowledge about the 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 select 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. We complete the analysis
with a study of the reasons behind this outperformance and an in-depth look at
the resulting embedding spaces.
