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@inproceedings{1377091,
author = {Ge, Mouzhi and Persia, Fabio},
address = {San Diego, USA},
booktitle = {Proceedings of the IEEE International Conference on Semantic Computing},
doi = {http://dx.doi.org/10.1109/ICSC.2017.31},
keywords = {Multimedia Recommendation; Multimedia Recommender Systems; Research Challenges},
howpublished = {elektronická verze "online"},
language = {eng},
location = {San Diego, USA},
isbn = {978-1-5090-4896-0},
pages = {344-347},
publisher = {IEEE},
title = {Research Challenges in Multimedia Recommender Systems},
url = {http://icsc.eecs.uci.edu},
year = {2017}
}
TY - JOUR
ID - 1377091
AU - Ge, Mouzhi - Persia, Fabio
PY - 2017
TI - Research Challenges in Multimedia Recommender Systems
PB - IEEE
CY - San Diego, USA
SN - 9781509048960
KW - Multimedia Recommendation
KW - Multimedia Recommender Systems
KW - Research Challenges
UR - http://icsc.eecs.uci.edu
N2 - 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.
ER -
GE, Mouzhi a Fabio PERSIA. Research Challenges in Multimedia Recommender Systems. Online. In \textit{Proceedings of the IEEE International Conference on Semantic Computing}. San Diego, USA: IEEE, 2017, s.~344-347. ISBN~978-1-5090-4896-0. Dostupné z: https://dx.doi.org/10.1109/ICSC.2017.31.
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