Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Hu, Ronglin"'
Publikováno v:
In Procedia Computer Science 2021 187:140-145
Akademický článek
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Autor:
Nishio, Takahiro, Hu, Ronglin, Koyama, Yukinori, Liang, Shuang, Rosenthal, Sara B., Yamamoto, Gen, Karin, Daniel, Baglieri, Jacopo, Ma, Hsiao-Yen, Xu, Jun, Liu, Xiao, Dhar, Debanjan, Iwaisako, Keiko, Taura, Kojiro, Brenner, David A., Kisseleva, Tatiana
Publikováno v:
In Journal of Hepatology September 2019 71(3):573-585
Autor:
Liang, Shuang, Ma, Hsiao-Yen, Zhong, Zhenyu, Dhar, Debanjan, Liu, Xiao, Xu, Jun, Koyama, Yukinori, Nishio, Takahiro, Karin, Daniel, Karin, Gabriel, Mccubbin, Ryan, Zhang, Cuili, Hu, Ronglin, Yang, Guizhi, Chen, Li, Ganguly, Souradipta, Lan, Tian, Karin, Michael, Kisseleva, Tatiana, Brenner, David A.
Publikováno v:
In Gastroenterology March 2019 156(4):1156-1172
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 20:4553-4566
Contextual information can accurately describe the user’s time, location, environment, activities, the use of devices, and so on. The recommender systems with contextual information have better prediction accuracy, coverage, and user satisfaction.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::360a343199136984c36408da50cfc6c0
https://doi.org/10.1109/tits.2018.2889069
https://doi.org/10.1109/tits.2018.2889069
Publikováno v:
International Journal of Grid and High Performance Computing; January 2023, Vol. 14 Issue: 1 p1-21, 21p
Publikováno v:
Proceedings of SPIE; December 2022, Vol. 12506 Issue: 1 p125063O-125063O-11, 12381249p
Publikováno v:
IEEE Access, Vol 6, Pp 45071-45085 (2018)
Context-aware recommender systems focus on improving recommendation accuracy by adding contextual information and have been widely used in the real-world applications. However, conventional context-aware recommendation approaches have the drawbacks o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3df3b162459cedb336fd92d7fc30ff80
https://ieeexplore.ieee.org/document/8435916/
https://ieeexplore.ieee.org/document/8435916/
Publikováno v:
Pattern Analysis and Applications. 22:633-647
Collaborative filtering (CF) recommendation has made great success in solving information overload. However, CF has some disadvantages such as cold start, data sparseness, low operation efficiency and knowledge cannot transfer between multiple rating
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31f806e41f5ca024441df7bd5883a1ed
https://doi.org/10.1007/s10044-017-0671-2
https://doi.org/10.1007/s10044-017-0671-2