• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2012, Vol. 34 ›› Issue (12): 160-163.

• 论文 • 上一篇    下一篇

基于云模型的时间修正协同过滤推荐算法

王晓堤,桑婧   

  1. (天津财经大学商学院管理信息系统系,天津 300222)
  • 收稿日期:2011-12-29 修回日期:2012-03-26 出版日期:2012-12-25 发布日期:2012-12-25

A Collaborative Filtering Recommendation Algorithm with TimeAdjusting Based on Cloud Model

WANG Xiaodi,SANG Jing   

  1. (Department of Management Information System,College of Commerce,
    Tianjin University of Finance and  Economics,Tianjin 300222,China)
  • Received:2011-12-29 Revised:2012-03-26 Online:2012-12-25 Published:2012-12-25

摘要:

针对传统的协同过滤推荐系统存在的数据稀疏性和忽略时间影响的问题,本文提出了基于云模型的时间修正协同过滤推荐算法,利用云模型建立用户对项目特征属性的偏好度,并建立指数时间函数对项目的评分相似度沿时间维加以修正。算法采用美国GroupLens项目组提供的数据集进行实验。结果表明,该算法使得项目的评分相似度度量更趋准确,系统推荐质量有较明显的提高。

关键词: 最近邻协同过滤推荐, 云模型, 项目的评分相似度, 时间修正

Abstract:

Aiming at the problem of data sparsity and time effects in the traditional collaborative filtering system,a Collaborative Filtering Recommendation Algorithm with TimeAdjusting Based on Cloud Model (CTCFR) is proposed.It creates the user's preference of items' attributes by using the cloud model,and adjusts the items rating similarity by establishing an exponential time function.Based on the data set from GroupLens project team,the experimental result shows that this algorithm can make the measurement of the items rating similarity more accurate and improve the quality of the recommendation better.

Key words: the nearest neighbor collaborative filtering;cloud model;items rating similarity;timeadjusting