書誌事項
- タイトル別名
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- Contribution Analysis of Video Advertising with Topic Model and Ensemble Learning
抄録
<p>With the widespread use of highly functional smartphones and the improvement of communication environments,video advertising is becoming widely used in the mobile advertising domain. When creators create videoadvertisements, if they know in advance the most effective components and combinations, they are more likely to beable to produce them more efficiently. For mobile ad images, [Sakihama 19b] interpreted the results of a click-rateprediction model using Gradient Boosted Decision Trees (GBDT) and Interpretable Trees (inTrees) [Deng 19].</p><p>In this paper, we propose a multimodal approach to analyzing the factors of advertising effectiveness, whichconsists of ad delivery logs, components of video ads, and text information. Specifically, we propose a method forverifying the effectiveness of video advertisements in mobile advertising based on computer vision and a method forsupporting the production of video advertisements using the modeling results of Latent Dirichlet Allocation (LDA),XgBoost [Chen 16], and defragTrees [Hara 18]. This method is expected to be faster and simpler than the oneproposed by [Sakihama 19b], and is likely to enable rule extraction. Computer vision and machine learning will enableautomatic feature extraction, identification of effective components and interactions, and contribution measurement.It is expected to be applied to a wide range of fields other than video advertising.</p>
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 36 (3), B-K91_1-8, 2021-05-01
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390006432293117824
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- NII論文ID
- 130008033275
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- ISSN
- 13468030
- 13460714
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可