A Fast Linear Computational Framework for User Action Prediction in Tencent MyApp

User action modeling and prediction has long been a topic of importance to recommender systems and user profiling. The quality of the model or accuracy of prediction plays a vital role in related applications like recommendation, advertisement displaying, searching, etc. For large scale systems with a massive number of users, beside the pure prediction performance, there are other practical factors like training and prediction latency, memory overhead, that must be optimized to ensure smooth operation of the system. We propose a fast linear computational framework to handle a vast number of second order crossed features with dimensionality reduction. By leveraging the training and serving system architecture, we shift heavy calculation burden from online serving to offline preprocessing, at the cost of a reasonable amount of memory overhead. The experiments on a 15-day data trace from Tencent MyApp shows that our proposed framework can achieve comparable prediction performance to much complex models like the field-aware factorization machine (FFM) while being served in 2 ms with a reasonable amount of memory overhead.