With the rising of short video apps, such as TikTok, Snapchat and Kwai, advertisement in short-term user-generated videos (UGVs) has become a trending form of advertising. Prediction of user behavior without specific user profile is required by advertisers, as they expect to acquire advertisement performance in advance in the scenario of cold start. Current recommender system do not take raw videos as input; additionally, most previous work of Multi-Modal Machine Learning may not deal with unconstrained videos like UGVs. In this paper, we proposed a novel end-to-end self-organizing framework for user behavior prediction. Our model is able to learn the optimal topology of neural network architecture, as well as optimal weights, through training data. We evaluate our proposed method on our in-house dataset. The experimental results reveal that our model achieves the best performance in all our experiments.
翻译:随着短视频应用程序(如TikTok、Snapchat和Kwai)的兴起,短期用户制作的视频广告(UGVs)已成为一种趋势式广告形式。广告商需要预测用户行为而不提供具体的用户概况,因为他们期望在寒冷的开端中提前获得广告性能。目前的建议系统不把原始视频作为输入;此外,多式机器学习的多数以往工作可能不会涉及UGVs等不受限制的视频。在本文中,我们提出了用户行为预测的新颖的端到端自我组织框架。我们的模型能够通过培训数据学习神经网络结构的最佳地形学和最佳重量。我们评估了我们内部数据集的拟议方法。实验结果显示,我们模型在所有实验中都取得了最佳的性能。