Hybrid collaborative filtering algorithm based on KNN-SVM
基于KNN-SVM的混合协同过滤推荐算法

The problem of data sparseness has great influence on collaborative filtering recommendation system's accuracy, balance for this missing data fusion method, this paper proposed a hybrid collaborative filtering algorithms based on KNN-SVM. K-nearest neighbor method used the training set to fill the missing data, and then cross-validated by SVM classification. Comprehend advantages both KNN and SVM in order to overcome impact of data quality on the recommended algorithm. The proposed approach was applied to benchmark problems, and the simulation results show it is valid.