Predicting Online Video Advertising Effects with Multimodal Deep Learning

TL;DR

This paper presents a multimodal deep learning framework to accurately predict the click-through rate of online video ads by leveraging video, text, and metadata features, addressing the challenge of effect prediction in video advertising.

Contribution

The study introduces an optimized multimodal deep learning approach that effectively separates and normalizes metadata types and employs regularization to improve CTR prediction accuracy.

Findings

Achieved a correlation coefficient of 0.695 in CTR prediction

Significant improvement over baseline with 0.487 correlation

Demonstrated the importance of multimodal features and regularization

Abstract

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).