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- W4313000428 abstract "This paper presents interest level estimation using behavior information through multi-view feature integration considering partial and ordered labels. User behavior information is effective for estimating interest levels on content. However, behavior information is noisy, and a noise-robust integration method is needed. We propose a novel multi-view feature integration method based on multi-modal Gaussian process latent variable model (mGPLVM). The effectiveness of our method is that it can apply interest levels assigned to some samples and the order of the levels to integrate content and behavior features. Consequently, we verify the effectiveness of our proposed method through the experiments." @default.
- W4313000428 created "2023-01-05" @default.
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- W4313000428 date "2022-07-05" @default.
- W4313000428 modified "2023-09-27" @default.
- W4313000428 title "Interest Level Estimation Using Behavior Information through Multi-view Feature Integration Considering Partial and Ordered Labels" @default.
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- W4313000428 doi "https://doi.org/10.1109/itc-cscc55581.2022.9894995" @default.
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