[1]李振博,徐桂琼,査九. 基于用户谱聚类的协同过滤推荐算法[J].计算机技术与发展,2014,24(09):59-62.
　LI Zhen-bo,XU Gui-qiong,ZHA Jiu. A Collaborative Filtering Recommendation Algorithm Based on User Spectral Clustering[J].,2014,24(09):59-62.
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 基于用户谱聚类的协同过滤推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年09期
页码:
59-62
栏目:
智能、算法、系统工程
出版日期:
2014-09-10

文章信息/Info

Title:
 A Collaborative Filtering Recommendation Algorithm Based on User Spectral Clustering
文章编号:
1673-629X（2014）09-0059-04
作者:
 李振博; 徐桂琼; 査九
 上海大学 管理学院
Author(s):
 LI Zhen-bo; XU Gui-qiong; ZHA Jiu
关键词:
 ; 协同过滤; 非负矩阵分解; 相似度; 谱聚类
Keywords:
 ; collaborative filtering; Non-negative Matrix Factorization ( NMF); similarity; spectral clustering
分类号:
TP391
文献标志码:
A
摘要:
 针对电子商务系统中传统协同过滤推荐算法面临的稀疏性、准确性、实时性等问题，提出了一种基于用户谱聚类的协同过滤推荐算法。首先利用非负矩阵分解的方法对原始稀疏评分矩阵进行平滑处理，然后利用改进相似度的谱聚类方法将用户聚类，最后在用户所属类中寻找最近邻并产生推荐。用户谱聚类过程可离线完成，加快了在线推荐速度。在数据集MovieLens上的实验结果表明，该算法在平均绝对偏差、召回率、准确率等方面都有了较大改善，提高了推荐质量。
Abstract:
 Abatract:Considering the sparsity,accuracy and the real-time problem of traditional collaborative filtering recommendation algorithms in electronic commerce system,a new collaborative filtering algorithm based on user spectral clustering is proposed. Firstly,it employs non-negative matrix factorization algorithm to fill the missing ratings. Then,it uses spectral clustering method of improved similarity to cluster users. Finally,it finds the nearest neighbors of the user according to the user’s cluster and generates recommendations. Spectral clustering can be performed by off-line,which will accelerate the speed of online recommendation. The experimental results on MovieLens show that the new algorithm improves recommendation quality in MAE,recall and precision.

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更新日期/Last Update: 2015-04-01