Video engagement is important in online advertisements where there is no physical interaction with the consumer. Engagement can be directly measured as the number of seconds after which a consumer skips an advertisement. In this paper, we propose a model to predict video engagement of an advertisement using only a few samples. This allows for early identification of poor quality videos. This can also help identify advertisement frauds where a robot runs fake videos behind the name of well-known brands. We leverage on the fact that videos with high engagement have similar viewing patterns over time. Hence, we can create a similarity network of videos and use a graph-embedding model called DeepWalk to cluster videos into significant communities. The learned embedding is able to identify viewing patterns of fraud and popular videos. In order to assess the impact of a video, we also consider how the view counts increase or decrease over time. This results in a heterogeneous graph where an edge indicates similar video engagement or history of view counts between two videos. Since it is difficult to find labelled samples for ‘fraud’ video, we leverage on a one-class model that can determine ‘fraud’ videos with outlier or abnormal behavior. The proposed model outperforms baselines in F-measure by over 20$\%$.

视频参与在与消费者没有物理互动的在线广告中很重要。参与度可以直接衡量为消费者跳过广告的秒数。在本文中，我们提出了一个模型来仅使用几个样本来预测广告的视频参与度。这允许及早识别低质量视频。这也有助于识别广告欺诈，其中机器人以知名品牌的名义播放虚假视频。我们利用具有高参与度的视频随着时间的推移具有相似的观看模式这一事实。因此，我们可以创建视频的相似性网络，并使用称为 DeepWalk 的图嵌入模型将视频聚类到重要的社区中。学习到的嵌入能够识别欺诈和流行视频的观看模式。为了评估视频的影响，我们还会考虑观看次数如何随时间增加或减少。这导致异构图，其中边表示两个视频之间相似的视频参与度或观看次数历史。由于很难找到“欺诈”视频的标记样本，因此我们利用一种可以确定具有异常或异常行为的“欺诈”视频的一类模型。所提出的模型在 F-measure 中优于基线超过 20 我们利用可以确定具有异常或异常行为的“欺诈”视频的一类模型。所提出的模型在 F-measure 中优于基线超过 20 我们利用可以确定具有异常或异常行为的“欺诈”视频的一类模型。所提出的模型在 F-measure 中优于基线超过 20$\%$.