基于矩阵分解与用户近邻模型的协同过滤推荐算法

Collaborative filtering and recommendation algorithm based on matrix factorization and user nearest neighbor model

针对个性化推荐系统中协同过滤算法面对的矩阵稀疏和新使用者问题,提出基于矩阵分解与用户近邻模型的推荐算法。通过对用户档案信息构建近邻模型以保证新使用者预测的准确性;同时考虑到数据量大和矩阵稀疏会引起时间和空间复杂度过高等问题,引入奇异值矩阵分解的方式,从而减小矩阵稀疏和数据量大的影响,提高推荐系统的准确性。实验结果表明,该算法能有效解决大数据量的矩阵稀疏问题以及新使用者问题。

Concerning the difficulty of data sparsity and new user problems in many collaborative recommendation algorithms, a new collaborative recommendation algorithm based on matrix factorization and user nearest neighbor was proposed. To guarantee the prediction accuracy of the new users, the user nearest neighbor model based on user data and profile information was used. Meanwhile, large data sets and the problem of matrix sparsity would significantly increase the time and space complexity. Therefore, matrix factorization was introduced to alleviate the effect of data problems and improve the prediction accuracy. The experimental results show that the new algorithm can improve the recommendation accuracy effectivelv, and solve the Droblems of data sparsity and new user.