| Presentation | 2020-03-05 Predicting the online video advertising effectiveness with multimodal deep learning Jun Ikeda, Hiroyuki Seshime, Xueting Wang, Toshihiko Yamasaki, Kiyoharu Aizawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this research, we propose a method for predicting the Click Through Rate of video ads and analyzing the factors that determine the Click Through Rate as a foothold for predicting the effects of video ads on the Internet to users. We have been conducting research on image banner ads and TV commercials, but in order to obtain high prediction accuracy for online video advertisements, it is necessary to optimize the architecture and parameters. As a result, the prediction accuracy of 0.695 was obtained. Additionally, we demonstrated that the first few seconds of the video, the last frame, and the metadata are the major factors of Click Through Rate. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Click Through Rate / CTR / deep learning / multi-modal / online video ad. |
| Paper # | IMQ2019-41,IE2019-123,MVE2019-62 |
| Date of Issue | 2020-02-27 (IMQ, IE, MVE) |
| Conference Information | |
| Committee | IE / IMQ / MVE / CQ |
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| Conference Date | 2020/3/5(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyushu Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Hideaki Kimata(NTT) / Toshiya Nakaguchi(Chiba Univ.) / Kenji Mase(Nagoya Univ.) / Hideyuki Shimonishi(NEC) |
| Vice Chair | Kazuya Kodama(NII) / Keita Takahashi(Nagoya Univ.) / Mitsuru Maeda(Canon) / Kenya Uomori(Osaka Univ.) / Masayuki Ihara(NTT) / Jun Okamoto(NTT) / Takefumi Hiraguri(Nippon Inst. of Tech.) |
| Secretary | Kazuya Kodama(NTT) / Keita Takahashi(NHK) / Mitsuru Maeda(Shizuoka Univ.) / Kenya Uomori(Sony Semiconductor Solutions) / Masayuki Ihara(Nagoya Univ.) / Jun Okamoto(NTT) / Takefumi Hiraguri(Nippon Inst. of Tech.) |
| Assistant | Kyohei Unno(KDDI Research) / Norishige Fukushima(Nagoya Inst. of Tech.) / Hiroaki Kudo(Nagoya Univ.) / Masaru Tsuchida(NTT) / Keita Hirai(Chiba Univ.) / Satoshi Nishiguchi(Oosaka Inst. of Tech.) / Masanori Yokoyama(NTT) / Shogo Fukushima(Univ. of ToKyo) / Chikara Sasaki(KDDI Research) / Yoshiaki Nishikawa(NEC) / Takuto Kimura(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Image Engineering / Technical Committee on Image Media Quality / Technical Committee on Media Experience and Virtual Environment / Technical Committee on Communication Quality |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Predicting the online video advertising effectiveness with multimodal deep learning |
| Sub Title (in English) | |
| Keyword(1) | Click Through Rate |
| Keyword(2) | CTR |
| Keyword(3) | deep learning |
| Keyword(4) | multi-modal |
| Keyword(5) | online video ad. |
| 1st Author's Name | Jun Ikeda |
| 1st Author's Affiliation | The University of Tokyo(UTokyo) |
| 2nd Author's Name | Hiroyuki Seshime |
| 2nd Author's Affiliation | The University of Tokyo(UTokyo) |
| 3rd Author's Name | Xueting Wang |
| 3rd Author's Affiliation | The University of Tokyo(UTokyo) |
| 4th Author's Name | Toshihiko Yamasaki |
| 4th Author's Affiliation | The University of Tokyo(UTokyo) |
| 5th Author's Name | Kiyoharu Aizawa |
| 5th Author's Affiliation | The University of Tokyo(UTokyo) |
| Date | 2020-03-05 |
| Paper # | IMQ2019-41,IE2019-123,MVE2019-62 |
| Volume (vol) | vol.119 |
| Number (no) | IMQ-454,IE-456,MVE-457 |
| Page | pp.pp.133-136(IMQ), pp.133-136(IE), pp.133-136(MVE), |
| #Pages | 4 |
| Date of Issue | 2020-02-27 (IMQ, IE, MVE) |