A NMF-based Collaborative Filtering Recommendation Algorithm

Recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering (CF) is one of the most important technologies in recommender systems. Such technology recommends items to a customer according to the preference data of similar customers. The performance of CF systems degrades with increasing number of customers and items. To reduce the dimensionality of filtering databases and to improve the performance, non-negative matrix factorization (NMF) is applied to CF. The experiment results show that NMF-based CF can improve the performance of CF systems in both the recommendation quality and efficiency.