TY - JOUR
T1 - Prediction of Online Video Advertising Inventory Based on TV Programs
T2 - A Deep Learning Approach
AU - Lee, So Hyun
AU - Yoon, Sang Hyeak
AU - Kim, Hee Woong
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Advertising inventory
KW - deep learning
KW - online video advertising
KW - prediction
KW - TV~programs
UR - http://www.scopus.com/inward/record.url?scp=85100809351&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3056115
DO - 10.1109/ACCESS.2021.3056115
M3 - Article
AN - SCOPUS:85100809351
SN - 2169-3536
VL - 9
SP - 22516
EP - 22527
JO - IEEE Access
JF - IEEE Access
M1 - 9343819
ER -