Movatterモバイル変換

关键词:推荐系统,协同过滤,聚类算法

Abstract:Collaborative filtering recommendation algorithm can use the known user preferences to predict the possible interest items,and it is now the most successful and widely used recommendation technique.However,traditional collaborative filtering recommendation algorithms suffer from data sparsity,which results in poor recommendation accuracy.Only user-item rating matrix has been used to data analysis by most current collaborative filtering algorithms,while the characteristics of the item attributes and user preferences for those who have not been considered.To address this issue,a collaborative filtering recommendation algorithm combing clustering and user preference was proposed in this paper.Firstly,the user preference matrix for the item category is constructed according to the user rating matrix and the item category information.Then the K-Means algorithm is used to cluster the item set,and the nearest-neighbor user corresponding to the unrated item is found based on the user preference matrix.Next,the sparse matrix in each item cluster is filled by the weight Slope One algorithm combined with the similarity of items to alleviate the problem of data sparsity.In addition,the user clusters are built based on the user interest matrix.Finally,the user based collaborative filtering algorithm in each user cluster is employed to predict the item rating for the filled rating matrix.The experimental results show that our algorithm effectively alleviates the sparsity problem of the raw rating matrix and achieves better quality of recommendation than other algorithms.

Key words:Recommender systems,Collaborative filtering,Clustering algorithm