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This paper presents a personalized preference estimation method for video recommendation. Our method not only uses deep convolutional neural network (DCNN)-based video features but also transforms them based on user's viewing behavior in order to improve accuracy of preference estimation for a video. Specifically, we adopt supervised multi-view canonical correlation analysis (sMVCCA) in order to calculate...
This paper presents a novel method for personalized video preference estimation based on early fusion using multiple users' viewing behavior. The proposed method adopts supervised Multi-View Canonical Correlation Analysis (sMVCCA) to estimate correlation between different types of features. Specifically, we estimate optimal projections maximizing the correlation between three features of video, target...
This paper presents novel video feature-based favorite video estimation method. In the proposed method, we use three features, videos, users' viewing behavior and users' evaluation scores for these videos. In order to calculate the novel video features, Multiset Canonical Correlations Analysis (MCCA) is applied to these features to integrate the different types of features. Specifically, MCCA maximizes...
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