2019
Advanced Recommender Systems by Exploiting Social Networks
GE, Mouzhi; Fabio PERSIA and Daniela D'AURIABasic 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.