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Predicting Online Video Advertising Effects with Multimodal Deep Learning
- Author / Creator
- Conferences
- ICPR 2020 MAIN CONFERENCE PS T5.7: Image processing and Superresolution (2021)
- Available as
- Online
- Summary
-
We propose a optimized framework for accurately predicting crick through rate of online video ads by taking advantage of the multimodal nature of online video ads including video, text, and metadat...
We propose a optimized framework for accurately predicting crick through rate of online video ads by taking advantage of the multimodal nature of online video ads including video, text, and metadata features. 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).
Details
- Creator
- Jun Ikeda
- Format
- Videos, Slides, Films
- Language
- English
- Contributors
- Publication
-
- New York, N.Y. : Underline, 2020
- Physical Details
-
- 1 online resource
- digital
- video file MPEG-4 Flash
- Event name: ICPR 2020 MAIN CONFERENCE.
- Event session: PS T5.7: Image processing and Superresolution.
- "Paper Number: 918".
- Originally produced by Jun Ikeda in 2021.
- Event name: ICPR 2020 MAIN CONFERENCE ; Event session: PS T5.7: Image processing and Superresolution, held on (January 11, 2021 14:00).
- System requirements: Adobe Flash Player.
- In English.