计算机应用研究2013，Vol.30Issue(3)：715-719,5.DOI:10.3969/j.issn.1001-3695.2013.03.018
基于二阶段相似度学习的协同过滤推荐算法
Collaborative filtering recommendation algorithm based on two stages of similarity learning
摘要
Abstract
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.c关键词
二阶段/相似度学习/协同过滤/既约梯度法/K-最近邻算法Key words
two stages/ similarity learning/ collaborative filtering/ reduced gradient method/ K-nearest neighbor( K-NN)分类
信息技术与安全科学引用本文复制引用
沈键,杨煜普..基于二阶段相似度学习的协同过滤推荐算法[J].计算机应用研究,2013,30(3):715-719,5.基金项目
国家"863"计划资助项目(2011AA040605) （2011AA040605）
