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Type: Journal article
Title: Deep Conversational Recommender Systems: Challenges and Opportunities
Author: Tran, D.H.
Sheng, Q.Z.
Zhang, W.E.
Hamad, S.A.
Khoa, N.L.D.
Tran, N.H.
Citation: Computer, 2022; 55(4):30-39
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Issue Date: 2022
ISSN: 0018-9162
Statement of
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
DOI: 10.1109/MC.2020.3045426
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Appears in Collections:Computer Science publications

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