Advanced Recommender Systems by Exploiting Social Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00112116" target="_blank" >RIV/00216224:14330/19:00112116 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/HCC46620.2019.00025" target="_blank" >http://dx.doi.org/10.1109/HCC46620.2019.00025</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/HCC46620.2019.00025" target="_blank" >10.1109/HCC46620.2019.00025</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced Recommender Systems by Exploiting Social Networks

Popis výsledku v původním jazyce

Social networks have become an indispensable part of our lives, which serve as communication channels, social interaction platforms as well as ubiquitous entertainment tools; meanwhile, social networks constantly generate big social media data that create decision complexity and information overload to users. As a result, recommender systems are emerged to suggest personalized and possibly preferred media for the users. However, social networks have extensively enriched the inputs for recommender systems, such as users' social relations, data source credibility, and new social media types. Consequently, this paper is aimed at identifying the crucial factors that can be used to advance recommender systems in social networks. For each factor, this paper discusses the state-of-the-art recommender system research in that aspect, and suggests how to integrate the featured data to build and improve recommender systems for social networks. The paper further proposes a model to integrate the crucial factors and indicates possible application domains for social media recommender systems.

Název v anglickém jazyce

Advanced Recommender Systems by Exploiting Social Networks

Popis výsledku anglicky

Social networks have become an indispensable part of our lives, which serve as communication channels, social interaction platforms as well as ubiquitous entertainment tools; meanwhile, social networks constantly generate big social media data that create decision complexity and information overload to users. As a result, recommender systems are emerged to suggest personalized and possibly preferred media for the users. However, social networks have extensively enriched the inputs for recommender systems, such as users' social relations, data source credibility, and new social media types. Consequently, this paper is aimed at identifying the crucial factors that can be used to advance recommender systems in social networks. For each factor, this paper discusses the state-of-the-art recommender system research in that aspect, and suggests how to integrate the featured data to build and improve recommender systems for social networks. The paper further proposes a model to integrate the crucial factors and indicates possible application domains for social media recommender systems.

Druh

D - Stať ve sborníku

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the IEEE International Conference on Humanized Computing and Communication

ISBN

9781728141251

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

118-125

Název nakladatele

IEEE

Místo vydání

Laguna Hills, CA, USA

Místo konání akce

Laguna Hills, CA, USA

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000525609700017