LAUSR

Rating prediction is a hot spot in the research of recommender systems. There are lots of methods in this field such as collaborative filtering. However, few of these approaches take users’ friendship relationships into consideration, which actually contain significant information for rating prediction. Besides, there exists natural noise in users’ ratings. In this paper, we propose a rating prediction algorithm named NF-SVM based on the analysis of users’ natural noise and relationships. We cluster users to sharpen the similarity attribute among users, and use an iterative algorithm to obtain the rank of users’ rating quality. Then, we analyze users’ rating history to obtain the attributes of users’ natural noise. All these attributes are used to build a training set for SVM to get a prediction model. We also tested our algorithm in a data set which is crawled down from Douban, one of the largest movie rating web sites in China. Then we compared our algorithm with other state-of-the-art rating prediction methods. Extensive experiments show that our algorithm outperforms the other algorithms.