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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/135306
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dc.contributor.authorTran, D.H.-
dc.contributor.authorSheng, Q.Z.-
dc.contributor.authorZhang, W.E.-
dc.contributor.authorHamad, S.A.-
dc.contributor.authorKhoa, N.L.D.-
dc.contributor.authorTran, N.H.-
dc.date.issued2022-
dc.identifier.citationComputer, 2022; 55(4):30-39-
dc.identifier.issn0018-9162-
dc.identifier.issn1558-0814-
dc.identifier.urihttps://hdl.handle.net/2440/135306-
dc.description.abstractUnlike 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.-
dc.description.statementofresponsibilityDai Hoang Tran and Quan Z. Sheng, Wei Emma Zhang, Salma Abdalla Hamad, Nguyen Lu Dang Khoa, Nguyen H. Tran-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
dc.rightsCopyright © 2022, IEEE-
dc.titleDeep Conversational Recommender Systems: Challenges and Opportunities-
dc.typeJournal article-
dc.identifier.doi10.1109/MC.2020.3045426-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP200102298-
pubs.publication-statusPublished-
dc.identifier.orcidZhang, W.E. [0000-0002-0406-5974]-
Appears in Collections:Computer Science publications

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