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arXiv · 2306.06302

Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application

Elan Markowitz·Ziyan Jiang·Fan Yang·Xing Fan·Tony Chen·Greg Ver Steeg·Aram Galstyan
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Abstract

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.

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Elan Markowitz, Ziyan Jiang, Fan Yang, Xing Fan, Tony Chen, Greg Ver Steeg, Aram Galstyan. 2023-06-09. Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application. https://doi.org/10.1109/icassp49660.2025.10889248

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