This work explores unifying knowledge enhanced recommendation with multi-domain recommendation systems in a conversational AI assistant application. Multi-domain recommendation leverages users' interactions in previous domains to improve recommendations in a new one. Knowledge graph enhancement seeks to use external knowledge graphs to improve recommendations within a single domain. Both research threads incorporate related information to improve the recommendation task. We propose 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 not be possible with either information source alone. We develop a new model and demonstrate the additive benefit of these approaches on a dataset derived from millions of users' queries for content across three domains (videos, music, and books) in a live virtual assistant application. We demonstrate significant improvement on overall recommendations as well as on recommendations for new users of a domain.
@article{arxiv.2306.06302,
title = {Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application},
author = {Elan Markowitz and Ziyan Jiang and Fan Yang and Xing Fan and Tony Chen and Greg Ver Steeg and Aram Galstyan},
journal= {arXiv preprint arXiv:2306.06302},
year = {2025}
}