Multi-level fast personalized recommendation system

Abstract

Personalized recommendation systems have high speed requirements, and most of the previous methods rely on powerful hardware to meet the real-time requirements. We algorithmically propose a fast recommender system. Our proposed method abandons the previous global comparison method, but uses a local comparison method, which greatly reduces the computational complexity. The experimental results also show that the effect of the method is not decreased, but improved.