Collaborative filtering recommendation algorithmbased on two stages of similarity learning
基于二阶段相似度学习的协同过滤推荐算法

In order to improve the accuracy of similarity calculation and recommendation performance in the traditional collaborative filtering recommender system, this paper proposed a collaborative filtering recommendation algorithm based on two stages of similarity learning. The algorithm took advantage of the nearest neighbor algorithm on the first stage to get candidate neighbors and used the reduced gradient method on the second stage to learn similarity. Eventually, the algorithm achieved a higher accuracy of similarity. The experimental results show that the proposed algorithm, on some conditions, not only outperforms the traditional method in terms of the error performance, but also has a fast convergence speed, which can make dynamic similarity adjustment and distributed calculation possible.