计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于用户近邻的N维张量分解推荐算法

陈健美,孙亚军   

  1. (江苏大学 计算机科学与通信工程学院,江苏 镇江 212013)
  • 收稿日期:2016-08-01 出版日期:2017-11-15 发布日期:2017-11-15
  • 作者简介:陈健美(1962—),女,教授、博士,主研方向为模式识别、医学图像处理与分析、数据挖掘;孙亚军,硕士研究生。

N Dimensional Tensor Decomposition Recommendation Algorithm Based on User’s Neighbors

CHEN Jianmei,SUN Yajun   

  1. (School of Computer Science and Telecommunication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China)
  • Received:2016-08-01 Online:2017-11-15 Published:2017-11-15

摘要: 基于张量分解的推荐算法存在推荐精度较低和数据稀疏的问题。为此,在传统的张量分解模型基础上,引入用户近邻信息,提出一种新的N维张量分解算法。利用上下文感知信息,把隐式反馈信息作为张量的第3维度,以建立N维张量分解模型,为进一步提高推荐质量,加入用户近邻信息来优化N维张量分解模型,以提高张量分解推荐算法的准确率。实验结果表明,融合用户近邻的张量分解推荐算法比传统的张量分解算法具有更好的准确性,能有效解决稀疏性和准确性问题。

关键词: 协同过滤算法, 反馈信息, 主成分分析, 张量分解, 推荐算法

Abstract: Recommendation algorithm based on tensor factorization has low accuracy and data sparseness problem.Therefore,on the basic of the traditional tensor decomposition model,this paper introduces the user nearest neighbor information,and proposes N dimensional tensor decomposition model.Using context aware information,it uses implicit feedback information as the third dimension to establish N dimensional tensor decomposition model.To further improve the the quality of recommendation,it adds the user nearest neighbor information to optimize the N dimensional tensor decomposition model to improve the accuracy of the tensor decomposition recommendation algorithm.Experimental results show that the tensor decomposition recommendation algorithm combined with user nearest neighbor has better accuracy than the traditional tensor decomposition algorithm,can effectively solve the sparsity and accuracy problems.

Key words: collaborative filtering algorithm, feedback information, Principal Component Analysis(PCA), tensor decomposition, recommendation algorithm

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