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
收藏
页码：30 / 39
页数：10
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