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 …

Date

April 6, 2025

Authors

Elan Markowitz, Ziyan Jiang, Fan Yang, Xing Fan, Zheng Chen, Greg Ver Steeg, Aram Galstyan

Conference

ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

1-5

Publisher

IEEE