D 2019

Advanced Recommender Systems by Exploiting Social Networks

GE, Mouzhi; Fabio PERSIA and Daniela D'AURIA

Basic information

Original name

Advanced Recommender Systems by Exploiting Social Networks

Authors

GE, Mouzhi (156 China, guarantor, belonging to the institution); Fabio PERSIA (380 Italy) and Daniela D'AURIA (380 Italy)

Edition

Laguna Hills, CA, USA, Proceedings of the IEEE International Conference on Humanized Computing and Communication, p. 118-125, 8 pp. 2019

Publisher

IEEE

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Publication form

printed version "print"

RIV identification code

RIV/00216224:14330/19:00112116

Organization unit

Faculty of Informatics

ISBN

978-1-72814-125-1

UT WoS

000525609700017

EID Scopus

2-s2.0-85078306123

Keywords in English

Recommender systems;Social media;Social networks

Tags

International impact, Reviewed
Changed: 6/5/2020 11:19, RNDr. Pavel Šmerk, Ph.D.

Abstract

In the original language

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.