Deep Conversational Recommender Systems: Challenges and Opportunities

被引：1

作者：

Dai Hoang Tran ^{[1
]}

Sheng, Quan Z. ^{[1
,2
]}

Zhang, Wei Emma ^{[3
]}

Hamad, Salma Abdalla ^{[1
]}

Khoa, Nguyen Lu Dang ^{[4
]}

Tran, Nguyen H. ^{[5
]}

机构：

[1] Macquarie Univ, Sydney, NSW, Australia

[2] Macquarie Univ, Dept Comp, Sydney, NSW, Australia

[3] Univ Adelaide, Sch Comp Sci, Adelaide, SA, Australia

[4] CSIRO, Data61, Analyt & Decis Sci Program, Sydney, NSW, Australia

[5] Univ Sydney, Sydney, NSW, Australia

来源：

COMPUTER | 2022年 / 55卷 / 04期

基金：

澳大利亚研究理事会;

关键词：

D O I：

10.1109/MC.2020.3045426

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. © 1970-2012 IEEE.

引用

页码：30 / 39

页数：10

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