论文标题
基于视频的简短广告评估系统：自组织学习方法
Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach
论文作者
论文摘要
随着Tiktok，Snapchat和Kwai等简短视频应用程序的上升，短期用户生成的视频（UGV）的广告已成为广告的趋势形式。广告商需要预测没有特定用户配置文件的用户行为，因为他们希望在冷启动的情况下预先获得广告性能。当前的推荐系统不会将原始视频作为输入；此外，大多数以前的多模式机器学习工作可能无法处理UGV等无约束视频。在本文中，我们为用户行为预测提出了一个新颖的端到端自组织框架。我们的模型能够通过培训数据学习神经网络架构的最佳拓扑以及最佳权重。我们在内部数据集上评估了我们提出的方法。实验结果表明，我们的模型在我们所有实验中都达到了最佳性能。
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.
