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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

63874-63894

Kód UT WoS článku

001021953000001

EID výsledku v databázi Scopus

2-s2.0-85160248301