Prediction of Online Video Advertising Inventory Based on TV Programs: A Deep Learning Approach

被引：7

作者：

Lee, So-Hyun ^{[1
]}

Yoon, Sang-Hyeak ^{[2
]}

Kim, Hee-Woong ^{[3
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China

[2] Smart Media Representat Co Ltd SMR, Seoul 03926, South Korea

[3] Yonsei Univ, Grad Sch Informat, Seoul 03722, South Korea

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Advertising; TV; Media; Deep learning; Predictive models; Forecasting; Prediction algorithms; Advertising inventory; online video advertising; TV~programs; deep learning; prediction;

D O I：

10.1109/ACCESS.2021.3056115

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the recent spread of digital content, patterns of media viewing have changed. This is especially true for programs formerly watched on TV but are now increasingly viewed through online videos. As more and more people watch online videos, the market for online video advertising is increasing. Including online video advertising, online advertising can be effective if advertisers and online service providers attract as many viewers as possible. In particular, service providers try to maximize their profits by efficiently selling advertising inventory, which indicates the volume of space available for advertisements. However, most of today's service providers use simple statistical applications to predict advertising inventory that leads to relatively inaccurate predictions. Therefore, this study aims to develop a model capable of accurately predicting advertising inventory and then validate the model. This study in predicting online video advertising inventory is based on using deep learning to analyze the raw data of online video channels and then comparing the results of these predictions with actual inventory, other results of machine learning techniques, and work-site method results. Using these techniques and approaches, future advertising inventory can be more accurately predicted. In addition, detailed strategies for the practice of online video advertising are suggested.

引用

页码：22516 / 22527

页数：12

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