Collaborative filtering recommendation algorithm based onnearest-neighborhood and rating support
基于评分支持度的最近邻协同过滤推荐算法

To solve the shortcomings of the traditional collaborative filtering recommendation algorithms, this paper proposed an improved collaborative filtering recommendation algorithm for the nearest neighbors based on rating support. First on the basis of correlation similarity, this algorithm adopted an improved similarity measure method which could dynamically adjust the value of similarity according to the modified common rating. Then, computed predicting rating and rating support of the active user and item based on the nearest neighbor sets. Finally, according to the rating support data, adjusted different self adaptive influence weights of the neighbor sets of the active user and the active item, and obtained the final recommendation results. The experimental results show that compared with the other recommendation algorithms, the algorithm can effectively avoid the defects of traditional similarity measure and improve the recommendation quality.