item subspace, rating support, collaborative filtering, sparsity, high dimensionality, Computer software, QA76.75-76.765, Technology (General), T1-995

Collaborative filtering(CF) recommendation algorithm is widely used because of its reasonable interpretability and simple process.However,datasets in recommendation systems have the characteristics of large scale,high sparsity and high dimensionality,which bring a great challenge for CF recommendation algorithms.To alleviate the above issues,this paper proposes a collaborative filtering recommendation algorithm based on the rating region subspace(RRS).According to the user-item rating matrix,RRS firstly divides the scoring range into three different regions:high scoring region,medium scoring region and low scoring region.On the basis of these three regions,each user finds its item subspaces,that is,high rating subspace,medium rating subspace and low rating subspace.A new similarity measurement method is defined to calculate the rating support between users in each region subspace.Only if the rating supports of users in all subspaces are high,the users are similar,which avoids the ra-ting interference of lazy users.Experimental results show that the proposed method can solve the issue of data sparsity to a certain extent,reduce the computational complexity and improve the recommendation performance,especially on high-dimensional datasets.