Factoring Personalization in Social Media Recommendations

D 2019

Factoring Personalization in Social Media Recommendations

GE, Mouzhi a Fabio PERSIA

Základní údaje

Originální název

Factoring Personalization in Social Media Recommendations

Autoři

GE, Mouzhi (156 Čína, garant, domácí) a Fabio PERSIA (380 Itálie)

Vydání

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

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/19:00108947

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5386-6783-5

ISSN

DOI

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

UT WoS

000467270600058

Klíčová slova anglicky

recommender systems; personalization

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 6. 5. 2020 12:44, RNDr. Pavel Šmerk, Ph.D.

Anotace

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

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