Preference estimation for video recommendation using DCNN features and viewing behavior

Yoshiki Ito, Takahiro Ogawa, Miki Haseyama

Abstract: 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 “canonical video features”, which have a maximal correlation between the following three kinds of features: a video, user's viewing behavior and user's evaluation scores for the video. By using the canonical video features, our method can estimate the user's personalized preference for a video more accurately than using only the DCNN-based video features. Experimental results show the effectiveness of our method.