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With the rising of short video apps, such as TikTok, Snapchat and Kwai,advertisement in short-term user-generated videos (UGVs) has become a trendingform of advertising. Prediction of user behavior without specific user profileis required by advertisers, as they expect to acquire advertisement performancein advance in the scenario of cold start. Current recommender system do nottake raw videos as input; additionally, most previous work of Multi-ModalMachine Learning may not deal with unconstrained videos like UGVs. In thispaper, we proposed a novel end-to-end self-organizing framework for userbehavior prediction. Our model is able to learn the optimal topology of neuralnetwork architecture, as well as optimal weights, through training data. Weevaluate our proposed method on our in-house dataset. The experimental resultsreveal that our model achieves the best performance in all our experiments.

Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach