  With expansion of the video advertising market, research to predict the
effects of video advertising is getting more attention. Although effect
prediction of image advertising has been explored a lot, prediction for video
advertising is still challenging with seldom research. In this research, we
propose a method for predicting the click through rate (CTR) of video
advertisements and analyzing the factors that determine the CTR. In this paper,
we demonstrate an optimized framework for accurately predicting the effects by
taking advantage of the multimodal nature of online video advertisements
including video, text, and metadata features. In particular, the two types of
metadata, i.e., categorical and continuous, are properly separated and
normalized. To avoid overfitting, which is crucial in our task because the
training data are not very rich, additional regularization layers are inserted.
Experimental results show that our approach can achieve a correlation
coefficient as high as 0.695, which is a significant improvement from the
baseline (0.487).
