Adelaide Research & Scholarship
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https://hdl.handle.net/2440/135306
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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 1558-0814 |
| Statement of Responsibility: | 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 |
| Grant ID: | http://purl.org/au-research/grants/arc/DP200102298 |
| Appears in Collections: | Computer Science publications |
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