Collaborative Filtering Algorithm Based on Improved Matrix Factorization and Spectral Clustering
A collaborative filtering algorithm based on improved matrix factorization and spectral clustering is proposed to address the issues of data sparsity,scalability,and accuracy in collaborative filtering algorithms.The algorithm first incorporates similarity calculation optimized by suppressing item popularity and user activity into the least squares method(ALS)to avoid the loss of factor information during matrix decomposition.Secondly,manifold learning algorithm based on spectral clustering is used to compensate for the high computational complexity caused by the ALS algorithm,while obtaining the global optimal solution to improve the accuracy of clustering the nearest neighbors of the target user.Finally,experiments are conducted using the Movielens dataset.The experimental results show that the improved algorithm can effectively reduce the average absolute error and root mean square error of the collaborative filtering algorithm,improve accuracy,and have better performance.
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