基于二阶段相似度学习的协同过滤推荐算法

针对传统的基于最近邻协同过滤推荐算法中计算相似度存在的缺陷,提出了一种基于二阶段相似度学习的协同过滤推荐算法,该算法旨在通过较少的迭代计算改善推荐算法性能。它以既约梯度法迭代寻优为主、最近邻算法为辅,通过邻居的海选和精选,最终提高了相似度的计算精度,改善了误差性能。实验表明,在一定条件下该算法不仅在误差性能上优于传统的推荐算法,而且其算法收敛速度快,可实现相似度参数动态调整和分布式计算。

Collaborative filtering recommendation algorithmbased on two stages of similarity learning

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.