Collaborative filtering algorithm based on matrix decomposition
Lei Li · Computer Engineering and Applications Journal · 2011
Collaborative filtering recommendation algorithm is one of the most successful technologies in the e-commerce recommendation system.Aiming at the problem that traditional collaborative filtering algorithms generally exist sparseness resistance and extendibility,in this paper,a CF algorithm,alternating-least-squares with weighted-λ-regularization(ALS-WR) is described.That is,by using regularization constraint to the traditional matrix decomposition model to prevent model overfitting training data and using alternating-least-squares method to train the decomposition model.The experimental evaluation using two real-world datasets shows that ALS-WR achieves better results in comparison with several classical collaborative filter-ing recommendation algorithms not only in extendibility but also in sparseness resistance.