Factoring Personalization in Social Media Recommendations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00108947" target="_blank" >RIV/00216224:14330/19:00108947 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICOSC.2019.8665624" target="_blank" >http://dx.doi.org/10.1109/ICOSC.2019.8665624</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICOSC.2019.8665624" target="_blank" >10.1109/ICOSC.2019.8665624</a>
Alternative languages
Result language
angličtina
Original language name
Factoring Personalization in Social Media Recommendations
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 13th IEEE International Conference on Semantic Computing
ISBN
9781538667835
ISSN
2325-6516
e-ISSN
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Number of pages
4
Pages from-to
344-347
Publisher name
IEEE
Place of publication
California, USA
Event location
California, USA
Event date
Jan 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000467270600058