Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018451" target="_blank" >RIV/62690094:18450/21:50018451 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-88113-9_2" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-88113-9_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-88113-9_2" target="_blank" >10.1007/978-3-030-88113-9_2</a>

Result language

angličtina

Original language name

Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review

Original language description

The increasing popularity of social networks indicates that the vast amounts of data contained within them could be useful in various implementations, including recommendation systems. Interests and research publications on deep learning-based recommendation systems have largely increased. This study aimed to identify, summarize, and assess studies related to the application of deep learning-based recommendation systems on social media platforms to provide a systematic review of recent studies and provide a way for further research to improve the development of deep learning-based recommendation systems in social environments. A total of 32 papers were selected from previous studies in five of the major digital libraries, including Springer, IEEE, ScienceDirect, ACM, Scopus, and Web of Science, published between 2016 and 2020. Results revealed that even though RS has received high coverage in recent years, several obstacles and opportunities will shape the future of RS for researchers. In addition, social recommendation systems achieving high accuracy can be built by using a combination of techniques that incorporate a range of features in SRS. Therefore, the adoption of deep learning techniques in developing social recommendation systems is undiscovered. © 2021, Springer Nature Switzerland AG.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10102 - Applied mathematics

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Communications in Computer and Information Science

ISBN

978-3-030-88112-2

ISSN

1865-0929

e-ISSN

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Number of pages

15

Pages from-to

15-29

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

Cham

Event location

On-line

Event date

Sep 29, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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