A recommendation algorithm integrating data mining andrating prediction
Aiming at the traditional UserCF algorithms'problems of inaccuracy,data sparsity,and high cost of similarity calculation,a recommendation algorithmDRR integrating data mining and rating prediction is designed to improve the recommendation accuracy,coverage and time efficiency.First,the PCA dimension reduction algorithm is used to solve the problem of the extra large and sparse user rating matrix.Second,the Canopy algorithm is used to process the reduced dimension matrix to obtain the number of clusters K.Then the K-means algorithm is deployed to cluster users with cosine similarity as the distance measurement,and the Apriori algorithm is adopted to mine the potential association rules between items in the cluster.Thus,the item association factor is calculated.Finally,other users in the target user's cluster are taken as neighbors,and the rating of the target user on the item is predicted according to the historical rating,cosine similarity and item correlation factor to mine the long tail items while reducing the time consumption of searching for the nearest neighbor.The experimental results on the movieLens dataset and the Douban movie dataset show that the accuracy,recall,F1 value,coverage and time efficiency of the DRR algorithm have been improved,compared with those of the UserCF algorithm and the K-means clustering based collaborative filtering algorithm,and the spectral clustering based collaborative filtering algorithm.
万方数据
维普
