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Recommender systems have found significant commercial success but stillstruggle with integrating new users. Since users often interact with content indifferent domains, it is possible to leverage a user's interactions in previousdomains to improve that user's recommendations in a new one (multi-domainrecommendation). A separate research thread on knowledge graph enhancement usesexternal knowledge graphs to improve single domain recommendations (knowledgegraph enhancement). Both research threads incorporate related information toimprove predictions in a new domain. We propose in this work to unify theseapproaches: Using information from interactions in other domains as well asexternal knowledge graphs to make predictions in a new domain that would beimpossible with either information source alone. We apply these ideas to adataset derived from millions of users' requests for content across threedomains (videos, music, and books) in a live virtual assistant application. Wedemonstrate the advantage of combining knowledge graph enhancement withprevious multi-domain recommendation techniques to provide better overallrecommendations as well as for better recommendations on new users of a domain.

Multi-Task Knowledge Enhancement for Zero-Shot and Multi-Domain Recommendation in an AI Assistant Application