Predicting Online Video Advertising Inventory based on Digital Content

Abstract

The viewing habits of media have changed with the advent of digital content. This is particularly true for programs that were once viewed on television but are now viewed online. Online video advertising is growing as more people view online videos. Online video advertising includes online video advertising. However, it is only possible to be effective if online service providers and advertisers attract as many viewers as possible. Service providers are motivated to maximize their profits by selling advertising inventory efficiently, which determines the amount of space that is available for advertisements. Unfortunately, many service providers today rely on simple statistical tools to predict advertising inventory. This results in inaccurate predictions. This study is designed to create a model that accurately predicts advertising inventory, and validate it. Deep learning is used to analyze online video channels' raw data and then compare the predictions with actual inventory, other machine learning results, and results from work-site methods. These techniques and methods can help predict future advertising inventory more accurately. Additionally, detailed strategies are recommended for online video advertising.