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

So Hyun Lee, Sang Hyeak Yoon, Hee Woong Kim

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Article number9343819
Pages (from-to)22516-22527
Number of pages12
JournalIEEE Access
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Materials Science(all)
  • Electrical and Electronic Engineering
  • Computer Science(all)


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