Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Deep Conversational Recommender Systems: Challenges and Opportunities|
|Citation:||Computer, 2022; 55(4):30-39|
|Publisher:||Institute of Electrical and Electronics Engineers (IEEE)|
|Dai Hoang Tran and Quan Z. Sheng, Wei Emma Zhang, Salma Abdalla Hamad, Nguyen Lu Dang Khoa, Nguyen H. Tran|
|Abstract:||Unlike traditional recommender systems, the conversational recommender system (CRS) models a user’s preferences through interactive dialogue conversations. Recently, deep learning approaches have been applied to CRSs, producing fruitful results. We discuss the development of deep CRSs and future research directions.|
|Rights:||Copyright © 2022, IEEE|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.