Prediction is widely employed to improve the number of video clicks and views, which are the key important indicators (KPIs) due to their contribution to revenue. The available predictive features, however, are generally limited as compared to the expected prediction capability from the algorithm side. Inspired by the intrinsic dependence among multiple clicks for the same video, we hypothesize that there exist some consistent effects involved in grouped click records. We then propose to recover such effects from the associated hidden features, which are likely to alleviate the insufficiency of features. The simulation studies are performed to elucidate how the derived grouped effects empower a model with additional discriminating capacity compared with the original one. The proposed methodology is further examined on the repository of PPTV (a leading video service provider in China) click records comprehensively. The results confirm the existence of the hypothesized effects and demonstrate their critical role in the performance improvement of video click prediction.

With the growth of the digital advertising market, it has become more important than ever to target the desired audiences. Among various demographic traits, gender information plays a key role in precisely targeting the potential consumers in online advertising and ecommerce. However, such personal information is generally unavailable to digital media sellers. In this paper, we propose a novel task-specific multi-task learning algorithm to predict users' gender information from their video viewing behaviors. To detect as many desired users as possible, while controlling the Type I error rate at a user-specified level, we further propose Bayes testing and decision procedures to efficiently identify male and female users, respectively. Comprehensive experiments have justified the effectiveness and reliability of our framework.