Predicting Online Video Advertising Effects with Multimodal Deep Learning
This research provides a method for advertisers to more accurately predict the effectiveness of their online video campaigns, potentially leading to improved ad performance and return on investment.
This paper addresses the challenge of predicting online video advertisement click-through rates (CTR) by proposing a multimodal deep learning framework. The framework integrates video, text, and metadata features, achieving a correlation coefficient of 0.695, a significant improvement over the baseline's 0.487.
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).