Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Tetsuya Kushima"'
Publikováno v:
IEEE Access, Vol 8, Pp 126109-126118 (2020)
A method for estimating interest levels from behavior features via tensor completion including adaptive similar user selection is presented in this paper. The proposed method focuses on a tensor that is suitable for data containing multiple contexts
Externí odkaz:
https://doaj.org/article/b27be29f950041d48412a34c65f8d04d
Publikováno v:
IEEE Access, Vol 7, Pp 148576-148585 (2019)
A novel method for interest level estimation based on tensor completion via feature integration for partially paired users' behavior and videos is presented in this paper. The proposed method defines a novel canonical correlation analysis (CCA) frame
Externí odkaz:
https://doaj.org/article/2f42dda27a8f45cfb2226ef99ff69ccd
Publikováno v:
IEEE Access. 7:148576-148585
A novel method for interest level estimation based on tensor completion via feature integration for partially paired users' behavior and videos is presented in this paper. The proposed method defines a novel canonical correlation analysis (CCA) frame
Publikováno v:
LifeTech
This paper presents a new method for estimation of users' interest levels using tensor completion with SemiCCA. The proposed method extracts new features maximizing correlation between features calculated from partially paired users' behavior and con
Publikováno v:
ICME
This paper presents a novel method for interest level estimation of items via matrix completion based on adaptive user matrix construction. The proposed method introduces a new criterion for adaptively constructing a user matrix that consists of user
Conference
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