关键词: 推荐系统, 协同过滤, 矩阵分解, 迭代最小二乘法（ALS）, 矩阵奇异值分解（SVD）

Abstract: 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 filtering recommendation algorithms not only in extendibility but also in sparseness resistance.

Key words: recommended systems, collaborative filtering, matrix decomposition, Alternating Least Square（ALS）, Sigular Value Decomposition（SVD）