Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application
Abstrak
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.
Topik & Kata Kunci
Penulis (7)
Elan Markowitz
Ziyan Jiang
F. Yang
Xing Fan
Tony Chen
G. V. Steeg
A. Galstyan
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2023
- Bahasa
- en
- Total Sitasi
- 1×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/ICASSP49660.2025.10889248
- Akses
- Open Access ✓