摘要
利用资源分配的原理提出一个基于有向图分割的推荐算法.通过二部图网络结构与资源分配方法的结合,建立了物品间关系的有向图,再利用非对称非负矩阵分解(Asymmetric Nonnegative Matrix Factorization,ANMF)分割此有向图,并将物品根据分割结果得出的物品间关联关系进行分类,并以此设置物品间的关联权重,最终实现对用户的Top-N物品推荐方案.实验结果表明,提出的算法提高了推荐准确率,并且能在一定程度上提高推荐多样性,降低推荐物品的流行性.
By using the principle of resource allocation, we propose a recommendation algorithm which is based on the partition of directed graph. The items directed graph is established by combining with the bipartite graphs network structure and resource allocation method, and is partitioned by the method of Asymmetric Nonnegative Matrix Factorization. Then we classify items by the relationship between them, set connection weights between the items and implement a recommendation from the Top-N items to the user. Experimental results show that the proposed algorithm can improve the recommendation accuracy and the recommendation diversity, and reduce the popularity of recommendation to a certain extent.
出处
《计算机系统应用》
2015年第12期196-203,共8页
Computer Systems & Applications
关键词
推荐算法
有向图
ANMF
推荐权重
recommendation algorithm
directed graph
ANMF
recommendation weight
