A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis

Marco Giacalone; Flora Amato; Francesco  Di Cicco; Mattia  Fonisto

doi:10.1007/978-3-030-99619-2_10

A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis

Marco Giacalone, Flora Amato, Francesco Di Cicco, Mattia Fonisto

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

Abstract

In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.

Original language	English
Title of host publication	AINA 2022: Advanced Information Networking and Applications
Editors	Leonard Barolli, Farookh Hussain, Tomoya Enokido
Publisher	Springer
Pages	104-114
Number of pages <span style="color:red"p> <font size="1.5"> ✽ </span> </font>	11
Volume	451
ISBN (Electronic)	978-3-030-99619-2
ISBN (Print)	978-3-030-99618-5
DOIs	https://doi.org/10.1007/978-3-030-99619-2_10
Publication status	Published - 2022

Publication series

Name	Lecture Notes in Networks and Systems

Bibliographical note

Funding Information:
Acknowledgments. This paper has been produced with the financial support of the Justice Programme of the European Union, 101046629 CREA2, JUST-2021-EJUSTICE, JUST2027 Programme. The contents of this report are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

Access to Document

10.1007/978-3-030-99619-2_10

Cite this

Giacalone, M., Amato, F., Di Cicco, F., & Fonisto, M. (2022). A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis. In L. Barolli, F. Hussain, & T. Enokido (Eds.), AINA 2022: Advanced Information Networking and Applications (Vol. 451, pp. 104-114). (Lecture Notes in Networks and Systems). Springer. https://doi.org/10.1007/978-3-030-99619-2_10

@inbook{76b9a9cfcab041e581f6ab6568f052a1,

title = "A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis",

abstract = "In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.",

author = "Marco Giacalone and Flora Amato and {Di Cicco}, Francesco and Mattia Fonisto",

note = "Funding Information: Acknowledgments. This paper has been produced with the financial support of the Justice Programme of the European Union, 101046629 CREA2, JUST-2021-EJUSTICE, JUST2027 Programme. The contents of this report are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.",

year = "2022",

doi = "10.1007/978-3-030-99619-2_10",

language = "English",

isbn = "978-3-030-99618-5",

volume = "451",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "104--114",

editor = "Leonard Barolli and Farookh Hussain and Tomoya Enokido",

booktitle = "AINA 2022: Advanced Information Networking and Applications",

}

A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis. / Giacalone, Marco; Amato, Flora; Di Cicco, Francesco ; Fonisto, Mattia .

AINA 2022: Advanced Information Networking and Applications. ed. / Leonard Barolli; Farookh Hussain; Tomoya Enokido. Vol. 451 Springer, 2022. p. 104-114 (Lecture Notes in Networks and Systems).

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

TY - CHAP

T1 - A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis

AU - Giacalone, Marco

AU - Amato, Flora

AU - Di Cicco, Francesco

AU - Fonisto, Mattia

N1 - Funding Information: Acknowledgments. This paper has been produced with the financial support of the Justice Programme of the European Union, 101046629 CREA2, JUST-2021-EJUSTICE, JUST2027 Programme. The contents of this report are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.

PY - 2022

Y1 - 2022

N2 - In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.

AB - In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.

UR - https://link.springer.com/chapter/10.1007/978-3-030-99619-2_10

UR - http://www.scopus.com/inward/record.url?scp=85128786261&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-99619-2_10

DO - 10.1007/978-3-030-99619-2_10

M3 - Chapter

SN - 978-3-030-99618-5

VL - 451

T3 - Lecture Notes in Networks and Systems

SP - 104

EP - 114

BT - AINA 2022: Advanced Information Networking and Applications

A2 - Barolli, Leonard

A2 - Hussain, Farookh

A2 - Enokido, Tomoya

PB - Springer

ER -

A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis

Abstract

Publication series

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this