Factoring Personalization in Social Media Recommendations

D 2019

Factoring Personalization in Social Media Recommendations

GE, Mouzhi and Fabio PERSIA

Basic information

Original name

Factoring Personalization in Social Media Recommendations

Authors

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

Edition

California, USA, Proceedings of the 13th IEEE International Conference on Semantic Computing, p. 344-347, 4 pp. 2019

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

printed version "print"

RIV identification code

RIV/00216224:14330/19:00108947

Organization unit

Faculty of Informatics

ISBN

978-1-5386-6783-5

ISSN

DOI

http://dx.doi.org/10.1109/ICOSC.2019.8665624

UT WoS

000467270600058

Keywords in English

recommender systems; personalization

Abstract

V originále

Nowadays, since social media sites and online social networks have created big media data, it is thus complex and time-consuming for users to find the preferred social media from a large media catalog. Social media recommender systems are therefore emerged to recommend personalized media objects. However, most media recommender systems only focus on one aspect of social media. It is lacking a big picture of how to build an effective social media recommender system. Therefore, this paper tackles this challenge first for specifying the distinct features of media object that can be used for recommender systems, and then discusses five critical aspects that can affect the design of social media recommender systems. This paper further indicates how to assemble these critical aspects and concludes that when we apply traditional recommender algorithms in the media context, those are the critical aspects to improve and optimize social media recommneder systems.

Citovat

GE, Mouzhi and Fabio PERSIA. Factoring Personalization in Social Media Recommendations. In Proceedings of the 13th IEEE International Conference on Semantic Computing. California, USA: IEEE, 2019, p. 344-347. ISBN 978-1-5386-6783-5. Available from: https://dx.doi.org/10.1109/ICOSC.2019.8665624.

@inproceedings{1475296,
   author = {Ge, Mouzhi and Persia, Fabio},
   address = {California, USA},
   booktitle = {Proceedings of the 13th IEEE International Conference on Semantic Computing},
   doi = {http://dx.doi.org/10.1109/ICOSC.2019.8665624},
   keywords = {recommender systems; personalization},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {California, USA},
   isbn = {978-1-5386-6783-5},
   pages = {344-347},
   publisher = {IEEE},
   title = {Factoring Personalization in Social Media Recommendations},
   year = {2019}
}

TY  - JOUR
ID  - 1475296
AU  - Ge, Mouzhi - Persia, Fabio
PY  - 2019
TI  - Factoring Personalization in Social Media Recommendations
PB  - IEEE
CY  - California, USA
SN  - 9781538667835
KW  - recommender systems
KW  - personalization
N2  - Nowadays, since social media sites and online social networks have created big media data, it is thus complex and time-consuming for users to find the preferred social media from a large media catalog. Social media recommender systems are therefore emerged to recommend personalized media objects. However, most media recommender systems only focus on one aspect of social media. It is lacking a big picture of how to build an effective social media recommender system. Therefore, this paper tackles this challenge first for specifying the distinct features of media object that can be used for recommender systems, and then discusses five critical aspects that can affect the design of social media recommender systems. This paper further indicates how to assemble these critical aspects and concludes that when we apply traditional recommender algorithms in the media context, those are the critical aspects to improve and optimize social media recommneder systems.
ER  -

GE, Mouzhi and Fabio PERSIA. Factoring Personalization in Social Media Recommendations. In \textit{Proceedings of the 13th IEEE International Conference on Semantic Computing}. California, USA: IEEE, 2019, p.~344-347. ISBN~978-1-5386-6783-5. Available from: https://dx.doi.org/10.1109/ICOSC.2019.8665624.

Detailed Information on Publication Record