A Hybrid Slope One Collaborative Filtering Algorithm Based on Nonnegative Matrix Factorization

Abstract

Collaborative Filtering algorithm is widely used in plentiful personal recommendation system. However, it has low accuracy prediction in sparse data set. Current mainstream collaborative filtering algorithm filter neighbor of target user by calculating similarity between users with co-rated ratings. Nonnegative Matrix factorization (NMF) has a good performance in solving sparsity problem. Manifold learning algorithms can identify and preserve the intrinsic geometrical structure of data. In order to get more accurate recommendation results, we propose a hybrid Slope One algorithm based on NMF. By constraining PNMF with graph regularization term, then we propose a weighted Slope One algorithm combined with neighborhood preserving PNMF. The hybrid algorithm has positive consequences for new data and can reduce computation complexity. Experimental show that optimized method has a good recommendation effect compared with tradition algorithm, it helps to solve the data sparsity problem and can improve the scalability.