Factoring Personalization in Social Media Recommendations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Factoring Personalization in Social Media Recommendations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Factoring Personalization in Social Media Recommendations

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 13th IEEE International Conference on Semantic Computing

ISBN

9781538667835

ISSN

2325-6516

e-ISSN

—

Počet stran výsledku

4

Strana od-do

344-347

Název nakladatele

IEEE

Místo vydání

California, USA

Místo konání akce

California, USA

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000467270600058