Novel video feature-based favorite video estimation using users' viewing behavior and evaluation

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 the sum of three kinds of correlations between three pairs of these features. Then the novel video features that represent the users' individual preference can be obtained by using the projection maximizing the three correlations. Finally, Supported Vector Ordinal Regression (SVOR) is trained by using the novel video features to estimate favorite videos. Experimental results show the effectiveness of our method.