  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.
