A Systematic Review of Recommendation System Based on Deep Learning Methods

Jingjing Wang; Lap Kei Lee; Nga In Wu

doi:10.1007/978-3-031-22018-0_12

A Systematic Review of Recommendation System Based on Deep Learning Methods

Jingjing Wang, Lap Kei Lee, Nga In Wu

Division of Languages and Communication (LC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Recommender Systems (RSs) play an essential role in assisting online users in making decisions and finding relevant items of their potential preferences or tastes via recommendation algorithms or models. This study aims to provide a systematic literature review of deep learning-based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the area. Several publications were gathered from the Web of Science digital library from 2012 to 2022. We systematically review the most commonly used models, datasets, and metrics in RSs. At last, we discuss the potential direction of the future work.

Original language	English
Title of host publication	International Conference on Cyber Security, Privacy and Networking, ICSPN 2022
Editors	Nadia Nedjah, Gregorio Martínez Pérez, B.B. Gupta
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	122-133
Number of pages	12
ISBN (Print)	9783031220173
DOIs	https://doi.org/10.1007/978-3-031-22018-0_12
Publication status	Published - 2023
Event	International Conference on Cyber Security, Privacy and Networking, ICSPN 2022 - Virtual, Online Duration: 9 Sept 2021 → 11 Sept 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	599 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Cyber Security, Privacy and Networking, ICSPN 2022
City	Virtual, Online
Period	9/09/21 → 11/09/21

Keywords

Deep learning
Recommender system
Survey
Systematic literature review

Access to Document

10.1007/978-3-031-22018-0_12

Cite this

Wang, J., Lee, L. K., & Wu, N. I. (2023). A Systematic Review of Recommendation System Based on Deep Learning Methods. In N. Nedjah, G. Martínez Pérez, & B. B. Gupta (Eds.), International Conference on Cyber Security, Privacy and Networking, ICSPN 2022 (pp. 122-133). (Lecture Notes in Networks and Systems; Vol. 599 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22018-0_12

Wang, Jingjing ; Lee, Lap Kei ; Wu, Nga In. / A Systematic Review of Recommendation System Based on Deep Learning Methods. International Conference on Cyber Security, Privacy and Networking, ICSPN 2022. editor / Nadia Nedjah ; Gregorio Martínez Pérez ; B.B. Gupta. Springer Science and Business Media Deutschland GmbH, 2023. pp. 122-133 (Lecture Notes in Networks and Systems).

@inproceedings{71274efc989243fdac9f1d01acf4e22c,

title = "A Systematic Review of Recommendation System Based on Deep Learning Methods",

abstract = "Recommender Systems (RSs) play an essential role in assisting online users in making decisions and finding relevant items of their potential preferences or tastes via recommendation algorithms or models. This study aims to provide a systematic literature review of deep learning-based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the area. Several publications were gathered from the Web of Science digital library from 2012 to 2022. We systematically review the most commonly used models, datasets, and metrics in RSs. At last, we discuss the potential direction of the future work.",

keywords = "Deep learning, Recommender system, Survey, Systematic literature review",

author = "Jingjing Wang and Lee, {Lap Kei} and Wu, {Nga In}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Cyber Security, Privacy and Networking, ICSPN 2022 ; Conference date: 09-09-2021 Through 11-09-2021",

year = "2023",

doi = "10.1007/978-3-031-22018-0_12",

language = "English",

isbn = "9783031220173",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "122--133",

editor = "Nadia Nedjah and {Mart{\'i}nez P{\'e}rez}, Gregorio and B.B. Gupta",

booktitle = "International Conference on Cyber Security, Privacy and Networking, ICSPN 2022",

}

Wang, J, Lee, LK & Wu, NI 2023, A Systematic Review of Recommendation System Based on Deep Learning Methods. in N Nedjah, G Martínez Pérez & BB Gupta (eds), International Conference on Cyber Security, Privacy and Networking, ICSPN 2022. Lecture Notes in Networks and Systems, vol. 599 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 122-133, International Conference on Cyber Security, Privacy and Networking, ICSPN 2022, Virtual, Online, 9/09/21. https://doi.org/10.1007/978-3-031-22018-0_12

A Systematic Review of Recommendation System Based on Deep Learning Methods. / Wang, Jingjing; Lee, Lap Kei; Wu, Nga In.
International Conference on Cyber Security, Privacy and Networking, ICSPN 2022. ed. / Nadia Nedjah; Gregorio Martínez Pérez; B.B. Gupta. Springer Science and Business Media Deutschland GmbH, 2023. p. 122-133 (Lecture Notes in Networks and Systems; Vol. 599 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Systematic Review of Recommendation System Based on Deep Learning Methods

AU - Wang, Jingjing

AU - Lee, Lap Kei

AU - Wu, Nga In

PY - 2023

Y1 - 2023

N2 - Recommender Systems (RSs) play an essential role in assisting online users in making decisions and finding relevant items of their potential preferences or tastes via recommendation algorithms or models. This study aims to provide a systematic literature review of deep learning-based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the area. Several publications were gathered from the Web of Science digital library from 2012 to 2022. We systematically review the most commonly used models, datasets, and metrics in RSs. At last, we discuss the potential direction of the future work.

AB - Recommender Systems (RSs) play an essential role in assisting online users in making decisions and finding relevant items of their potential preferences or tastes via recommendation algorithms or models. This study aims to provide a systematic literature review of deep learning-based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the area. Several publications were gathered from the Web of Science digital library from 2012 to 2022. We systematically review the most commonly used models, datasets, and metrics in RSs. At last, we discuss the potential direction of the future work.

KW - Deep learning

KW - Recommender system

KW - Survey

KW - Systematic literature review

UR - http://www.scopus.com/inward/record.url?scp=85149628490&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/e1bfff86-de17-3b6a-8a16-c6925733a8f5/

U2 - 10.1007/978-3-031-22018-0_12

DO - 10.1007/978-3-031-22018-0_12

M3 - Conference contribution

AN - SCOPUS:85149628490

SN - 9783031220173

T3 - Lecture Notes in Networks and Systems

SP - 122

EP - 133

BT - International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

A2 - Nedjah, Nadia

A2 - Martínez Pérez, Gregorio

A2 - Gupta, B.B.

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

Y2 - 9 September 2021 through 11 September 2021

ER -

Wang J, Lee LK, Wu NI. A Systematic Review of Recommendation System Based on Deep Learning Methods. In Nedjah N, Martínez Pérez G, Gupta BB, editors, International Conference on Cyber Security, Privacy and Networking, ICSPN 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 122-133. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-22018-0_12