Interest Level Estimation Based on Feature Integration Considering Distribution of Partially Paired User's Behavior, Videos and Posters

Kyohei Kamikawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

Abstract: This paper presents a method for interest level estimation based on feature integration considering distribution of partially paired user's behavior, videos and posters. The proposed method collaboratively uses videos, their corresponding poster images, and user's behavior to estimate the interest levels. For dealing with the multi-view data, the proposed method newly derives semi-supervised divergence-aware multi-set canonical correlation analysis (SDMCCA). SDMCCA has two contributions. First, since the number of viewable videos is limited, consideration of unviewed videos is necessary, and SDMCCA adopts the semi-supervised approach. Second, information which data have is different due to the difference between similarities to other data. SDMCCA can simultaneously realize the above points and realize successful interest level estimation. Experimental results show the effectiveness of the proposed method.