How to exploit Recommender Systems in Social Media
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00103078" target="_blank" >RIV/00216224:14330/18:00103078 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/IRI.2018.00085" target="_blank" >http://dx.doi.org/10.1109/IRI.2018.00085</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IRI.2018.00085" target="_blank" >10.1109/IRI.2018.00085</a>
Alternative languages
Result language
angličtina
Original language name
How to exploit Recommender Systems in Social Media
Original language description
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.
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
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
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 IEEE 19th International Conference on Information Reuse and Integration for Data Science
ISBN
9781538626597
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
537-541
Publisher name
IEEE
Place of publication
Salt Lake City
Event location
Salt Lake City, Utah, USA
Event date
Jan 1, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000442457000077