Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review, Taxonomy, Issues, and Future Directions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020517" target="_blank" >RIV/62690094:18450/23:50020517 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10128133" target="_blank" >https://ieeexplore.ieee.org/document/10128133</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3276988" target="_blank" >10.1109/ACCESS.2023.3276988</a>

Result language

angličtina

Original language name

Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review, Taxonomy, Issues, and Future Directions

Original language description

Due to the rise of social media, a vast volume of information is shared daily. Finding relevant and acceptable information has become more challenging as the Internet&apos;s information flow has changed and more options have been available. Various recommendation systems have been proposed and successfully used for different applications. This paper presents a taxonomy of deep learning algorithms for social recommendation by examining selected papers using a systematic literature review approach. Forty-six publications were chosen from research published between 2016 and 2022 in six major online libraries. The main purpose of this research is to provide a brief review of published studies to assist future researchers in establishing new strategies in this field. The implantation of deep learning in recommender systems proved to be very effective and achieved competitive performance. Different methods and domains have been summarized to find the most appropriate method and domain.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

May

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

63874-63894

UT code for WoS article

001021953000001

EID of the result in the Scopus database

2-s2.0-85160248301