基于矩阵分解与用户近邻模型的协同过滤推荐算法

doi:10.3724/SP.J.1087.2012.00395

计算机应用 ›› 2012, Vol. 32 ›› Issue (02): 395-398.DOI: 10.3724/SP.J.1087.2012.00395

基于矩阵分解与用户近邻模型的协同过滤推荐算法

杨阳,向阳,熊磊

同济大学电子与信息工程学院，上海 201804

收稿日期:2011-07-12 修回日期:2011-09-06 发布日期:2012-02-23 出版日期:2012-02-01
通讯作者: 杨阳
作者简介:杨阳(1987-)，男，福建福州人，硕士研究生，主要研究方向：数据仓库、数据挖掘、协同过滤；
向阳(1962-),男,重庆人,教授,博士生导师,博士,主要研究方向：决策支持系统、人工智能；
熊磊(1989-)，男，江西丰城人，硕士研究生，主要研究方向：数据仓库、数据挖掘、协同过滤。
基金资助:
国家自然科学基金资助项目(61103069,71171148,70771077);上海市信息化发展专项资金项目(200901015)

Collaborative filtering and recommendation algorithm based on matrix factorization and user nearest neighbor model

YANG Yang,XIANG Yang,XIONG Lei

College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

Received:2011-07-12 Revised:2011-09-06 Online:2012-02-23 Published:2012-02-01
Contact: YANG Yang

摘要/Abstract

摘要： 针对个性化推荐系统中协同过滤算法面对的矩阵稀疏和新使用者问题，提出基于矩阵分解与用户近邻模型的推荐算法。通过对用户档案信息构建近邻模型以保证新使用者预测的准确性；同时考虑到数据量大和矩阵稀疏会引起时间和空间复杂度过高等问题，引入奇异值矩阵分解的方式，从而减小矩阵稀疏和数据量大的影响，提高推荐系统的准确性。实验结果表明，该算法能有效解决大数据量的矩阵稀疏问题以及新使用者问题。

关键词: 协同过滤, 矩阵分解, 用户近邻模型, 电子商务, 推荐算法

Abstract: Concerning the difficulty of data sparsity and new user problems in many collaborative recommendation algorithms, a new collaborative recommendation algorithm based on matrix factorization and user nearest neighbor was proposed. To guarantee the prediction accuracy of the new users, the user nearest neighbor model based on user data and profile information was used. Meanwhile, large data sets and the problem of matrix sparsity would significantly increase the time and space complexity. Therefore, matrix factorization was introduced to alleviate the effect of data problems and improve the prediction accuracy. The experimental results show that the new algorithm can improve the recommendation accuracy effectively, and solve the problems of data sparsity and new user.

Key words: collaborative filtering, matrix factorization, user nearest neighbor model, E-commerce, recommendation algorithm

中图分类号:

TP181

杨阳向阳熊磊. 基于矩阵分解与用户近邻模型的协同过滤推荐算法[J]. 计算机应用, 2012, 32(02): 395-398.

YANG Yang XIANG Yang XIONG Lei. Collaborative filtering and recommendation algorithm based on matrix factorization and user nearest neighbor model[J]. Journal of Computer Applications, 2012, 32(02): 395-398.

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基于矩阵分解与用户近邻模型的协同过滤推荐算法

Collaborative filtering and recommendation algorithm based on matrix factorization and user nearest neighbor model

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