  Recommender systems have found significant commercial success but still
struggle with integrating new users. Since users often interact with content in
different domains, it is possible to leverage a user's interactions in previous
domains to improve that user's recommendations in a new one (multi-domain
recommendation). A separate research thread on knowledge graph enhancement uses
external knowledge graphs to improve single domain recommendations (knowledge
graph enhancement). Both research threads incorporate related information to
improve predictions in a new domain. We propose in this work to unify these
approaches: Using information from interactions in other domains as well as
external knowledge graphs to make predictions in a new domain that would be
impossible with either information source alone. We apply these ideas to a
dataset derived from millions of users' requests for content across three
domains (videos, music, and books) in a live virtual assistant application. We
demonstrate the advantage of combining knowledge graph enhancement with
previous multi-domain recommendation techniques to provide better overall
recommendations as well as for better recommendations on new users of a domain.
