Research Challenges in Multimedia Recommender Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00096405" target="_blank" >RIV/00216224:14330/17:00096405 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICSC.2017.31" target="_blank" >http://dx.doi.org/10.1109/ICSC.2017.31</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICSC.2017.31" target="_blank" >10.1109/ICSC.2017.31</a>
Alternative languages
Result language
angličtina
Original language name
Research Challenges in Multimedia Recommender Systems
Original language description
Nowadays, since multimedia information has been extensively growing from a variety of sources, such photos from social networks, unstructured text from different websites, or raw video feed from digital sensors, multimedia recommender system has been emerging as a tool to help users choose which multimedia objects might be interesting for them. However, given the complexity of multimedia, it is still challenging to provide effective recommendations, and research so far could only address limited aspects. Therefore, in this paper we propose a set of research challenges, which can be used to implicate the future research directions for multimedia recommender systems. For each research challenge, we have also provided the insights to explain which aspects are worth further investigation.
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
2017
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 International Conference on Semantic Computing
ISBN
9781509048960
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
344-347
Publisher name
IEEE
Place of publication
San Diego, USA
Event location
San Diego, USA
Event date
Jan 1, 2017
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
000403391300065