Multi-Task Knowledge Enhancement for Zero-Shot and Multi-Domain
  Recommendation in an AI Assistant Application
Elan Markowitz Ziyan Jiang Fan Yang Xing Fan Tony Chen Greg Ver Steeg Aram Galstyan
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.