Gaussian iteration: A novel way to collaborative filtering

Abstract

Based on the missing not at random assumption and central limit theorem, this paper presents a novel way to accelerate the iteration speed in the collaborative filtering models called Gaussian iteration. In the proposed model, adding the Gaussian distribution to the estimation error makes the falling direction more credible, which significantly reduces the running time with the ideal accuracy. For evaluation, we compare the performance of the proposed model with three existing collaborative filtering models on two kinds of Movielens datasets. The results indicate that the novel method outperforms the existing models and it is easy to implement and faster. Moreover, the proposed model is scalable to the analogous objective function in other models.