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.

翻译：推荐系统已取得显著的商业成功，但在整合新用户方面仍面临挑战。由于用户常与不同领域的内容交互，可利用用户在先前领域的交互来改进其在新领域的推荐（多域推荐）。另一条独立研究线索——知识图增强——利用外部知识图改进单域推荐（知识图增强）。这两条研究线索均通过整合相关信息来提升新领域的预测性能。本文提出统一这些方法：结合其他领域的交互信息与外部知识图，在仅有单一信息来源时无法实现的新领域中进行预测。我们将这些思想应用于一个来自真实虚拟助手应用的百万级用户跨三域（视频、音乐、书籍）内容请求数据集。实验证明，将知识图增强与现有跨域推荐技术相结合，不仅能提供更优的整体推荐效果，还能显著改进领域新用户的推荐质量。