How to exploit Recommender Systems in Social Media

D 2018

How to exploit Recommender Systems in Social Media

PERSIA, Fabio, Mouzhi GE and Daniela D'AURIA

Basic information

Original name

How to exploit Recommender Systems in Social Media

Authors

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

Edition

Salt Lake City, Proceedings of the IEEE 19th International Conference on Information Reuse and Integration for Data Science, p. 537-541, 5 pp. 2018

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/18:00103078

Organization unit

Faculty of Informatics

ISBN

978-1-5386-2659-7

DOI

http://dx.doi.org/10.1109/IRI.2018.00085

UT WoS

000442457000077

Keywords in English

social media; recommender system; media recommendations; social media applications

Abstract

V originále

The rapid increase and widespread of social media data have created new research challenges and opportunities for social media recommender systems, which are designed to recommend personalized, interesting, credible social media content with possible social impact. However, due to complexity in social network and new media interaction, the research of social media recommender systems is still on its initial stage. Therefore, this paper aims to review the state-of-the-art research that are related to social media recommender systems, and identify the critical factors for building new social media recommender systems. Our results show that relevance, validity, popularity, credibility and social impact are considered to be the 5 important factors for social media recommender systems.

Citovat

PERSIA, Fabio, Mouzhi GE and Daniela D'AURIA. How to exploit Recommender Systems in Social Media. Online. In Proceedings of the IEEE 19th International Conference on Information Reuse and Integration for Data Science. Salt Lake City: IEEE, 2018, p. 537-541. ISBN 978-1-5386-2659-7. Available from: https://dx.doi.org/10.1109/IRI.2018.00085.

@inproceedings{1421616,
   author = {Persia, Fabio and Ge, Mouzhi and D'Auria, Daniela},
   address = {Salt Lake City},
   booktitle = {Proceedings of the IEEE 19th International Conference on Information Reuse and Integration for Data Science},
   doi = {http://dx.doi.org/10.1109/IRI.2018.00085},
   keywords = {social media; recommender system; media recommendations; social media applications},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Salt Lake City},
   isbn = {978-1-5386-2659-7},
   pages = {537-541},
   publisher = {IEEE},
   title = {How to exploit Recommender Systems in Social Media},
   year = {2018}
}

TY  - JOUR
ID  - 1421616
AU  - Persia, Fabio - Ge, Mouzhi - D'Auria, Daniela
PY  - 2018
TI  - How to exploit Recommender Systems in Social Media
PB  - IEEE
CY  - Salt Lake City
SN  - 9781538626597
KW  - social media
KW  - recommender system
KW  - media recommendations
KW  - social media applications
N2  - The rapid increase and widespread of social media data have created new research challenges and opportunities for social media recommender systems, which are designed to recommend personalized, interesting, credible social media content with possible social impact. However, due to complexity in social network and new media interaction, the research of social media recommender systems is still on its initial stage. Therefore, this paper aims to review the state-of-the-art research that are related to social media recommender systems, and identify the critical factors for building new social media recommender systems. Our results show that relevance, validity, popularity, credibility and social impact are considered to be the 5 important factors for social media recommender systems.
ER  -

PERSIA, Fabio, Mouzhi GE and Daniela D'AURIA. How to exploit Recommender Systems in Social Media. Online. In \textit{Proceedings of the IEEE 19th International Conference on Information Reuse and Integration for Data Science}. Salt Lake City: IEEE, 2018, p.~537-541. ISBN~978-1-5386-2659-7. Available from: https://dx.doi.org/10.1109/IRI.2018.00085.

Detailed Information on Publication Record