Building Effective Short Video Recommendation
Yang Liu, Cheng Lyu, Zhiyuan Liu, Dacheng Tao · 2019
How to build an effective personalized recommendation system is a challenging but highly valuable problem in social media services. This paper focuses on constructing a universal framework for short video recommendation by predicting the probability of finishing watching the entire video and pressing the 'like' button. Four novel techniques are proposed to improve the prediction accuracy. Firstly, we present an Incremental Multi-Window Scanning approach to extract the features pertaining to the users' behaviors. Also, a User Interaction Behavior Hierarchy is designed to capture a larger quantity of information and reduce the computing time. Additionally, the model transfer is capable of transferring the knowledge learned by the model on other datasets to the final model. Lastly, a rank-based ensemble approach which is suitable for tasks based on the evaluation metric of AUC is proposed. Our method long ranked first in the final stage of ICME Short Video Understanding Challenge (Track1) before the revision of competition rule.