Deep conversational recommender systems: challenges and opportunities

Dai Hoang Tran; Quan Z. Sheng; Wei Emma Zhang; Salma Abdalla Hamad; Nguyen Lu Dang Khoa; Nguyen H. Tran

doi:10.1109/MC.2020.3045426

Deep conversational recommender systems: challenges and opportunities

Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang, Salma Abdalla Hamad, Nguyen Lu Dang Khoa, Nguyen H. Tran

School of Computing

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

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.

Original language	English
Pages (from-to)	30-39
Number of pages	10
Journal	Computer
Volume	55
Issue number	4
DOIs	https://doi.org/10.1109/MC.2020.3045426
Publication status	Published - Apr 2022

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10.1109/MC.2020.3045426

Cite this

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title = "Deep conversational recommender systems: challenges and opportunities",

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. ",

author = "Tran, \{Dai Hoang\} and Sheng, \{Quan Z.\} and Zhang, \{Wei Emma\} and Hamad, \{Salma Abdalla\} and Khoa, \{Nguyen Lu Dang\} and Tran, \{Nguyen H.\}",

year = "2022",

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doi = "10.1109/MC.2020.3045426",

language = "English",

volume = "55",

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T1 - Deep conversational recommender systems

T2 - challenges and opportunities

AU - Tran, Dai Hoang

AU - Sheng, Quan Z.

AU - Zhang, Wei Emma

AU - Hamad, Salma Abdalla

AU - Khoa, Nguyen Lu Dang

AU - Tran, Nguyen H.

PY - 2022/4

Y1 - 2022/4

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85128826495

UR - http://purl.org/au-research/grants/arc/DP200102298

U2 - 10.1109/MC.2020.3045426

DO - 10.1109/MC.2020.3045426

M3 - Article

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SN - 0018-9162

VL - 55

SP - 30

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JO - Computer

JF - Computer

IS - 4

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Deep conversational recommender systems: challenges and opportunities

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What Can You Trust in the Large and Noisy Web?

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Deep conversational recommender systems: challenges and opportunities

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What Can You Trust in the Large and Noisy Web?

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