Purpose

Micro-video platforms have gained attention in recent years and have also become an important new channel for merchants to advertise their products. Since little research has studied micro-video advertising, this paper aims to fill the research gap by exploring the determinants of micro-video advertising clicks. We form a micro-video advertising click prediction model and demonstrate the effectiveness of the multimodal information extracted from the advertisement producers, commodities being sold and micro-video contents in the prediction task.

Design/methodology/approach

A multimodal analysis framework was conducted based on real-world micro-video advertisement datasets. To better capture the relations between different modalities, we adopt a cooperative learning model to predict the advertising clicks.

Findings

The experimental results show that the features extracted from different data sources can improve the prediction performance. Furthermore, the combination of different modal features (visual, acoustic, textual and numerical) is also worth studying. Compared to classical baseline models, the proposed cooperative learning model significantly outperforms the prediction results, which demonstrates that the relations between modalities are also important in advertising micro-video generation.

Originality/value

To the best of our knowledge, this is the first study analysing micro-video advertising effects. With the help of our advertising click prediction model, advertisement producers (merchants or their partners) can benefit from generating more effective micro-video advertisements. Furthermore, micro-video platforms can apply our prediction results to optimise their advertisement allocation algorithm and better manage network traffic. This research can be of great help for more effective development of the micro-video advertisement industry.

中文翻译：

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微视频广告点击预测的多模态协同学习

目的

微视频平台近年来备受关注，也成为商家宣传产品的重要新渠道。由于对微视频广告的研究很少，本文旨在通过探索微视频广告点击的决定因素来填补研究空白​​。我们形成了一个微视频广告点击预测模型，并证明了从广告制作者、销售商品和微视频内容中提取的多模态信息在预测任务中的有效性。

设计/方法/方法

基于真实世界的微视频广告数据集进行了多模态分析框架。为了更好地捕捉不同模式之间的关系，我们采用合作学习模型来预测广告点击。

发现

实验结果表明，从不同数据源中提取的特征可以提高预测性能。此外，不同模态特征（视觉、听觉、文本和数字）的组合也值得研究。与经典基线模型相比，所提出的合作学习模型显着优于预测结果，这表明模态之间的关系在广告微视频生成中也很重要。

原创性/价值

据我们所知，这是第一项分析微视频广告效果的研究。借助我们的广告点击预测模型，广告制作者（商家或其合作伙伴）可以从生成更有效的微视频广告中受益。此外，微视频平台可以应用我们的预测结果来优化其广告分配算法并更好地管理网络流量。本研究对微视频广告产业的更有效发展有很大帮助。