Collaborative Filtering Based on Star Users

Abstract

As one of the most popular recommender system technologies, neighborhood-based collaborative filtering algorithm has obtained great favor due to its simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the accuracy of similarity can be easily affected by noise and sparsity. In this paper, we present a novel collaborative filtering method based on user stars. Instead of treating every user as the same, we propose a method to generate a small number of users as the most reliable \emph{star users} and then produce predictions for the general population based on star users' ratings. Empirical studies on two different datasets suggest that our method outperforms traditional neighborhood-based collaborative filtering algorithm in terms of both efficiency and accuracy.