计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 398-401.

• 大数据与数据挖掘 • 上一篇    下一篇

融合评分差异和兴趣相似性的协同过滤推荐算法

魏慧娟,戴牡红   

  1. 湖南大学信息科学与工程学院 长沙410082
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:魏慧娟(1992-),女,硕士生,主要研究方向为数据挖掘,E-mail:2435768798@qq.com;戴牡红(1964-),男,研究员,主要研究方向为数据科学,E-mail:dmh@hnu.edu.cn(通信作者)。

Collaboration Filtering Recommendation Algorithm Based on Ratings Difference
and Interest Similarity

WEI Hui-juan, DAI Mu-hong   

  1. College of Information Science and Engineering,Hunan University,Changsha 410082,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 为了解决在传统的协同过滤推荐算法中存在的相似性计算不准确的问题,并提高推荐系统的质量,提出一种用户相似度计算方法。在用户共同评分的基础上,该方法根据评分差值和时间特征来计算评分差值的信息熵;然后,利用用户评分差值的信息熵和评分项目属性计算出用户的相似度;最后,根据用户相似度计算出用户的最近邻居,以此预测目标项目的评分。实验结果表明,所提算法更加准确地实现了目标用户最近邻居的查找,有效地提高了推荐的准确性。

关键词: 共同评分, 相似性计算, 项目属性, 协同过滤

Abstract: In order to improve the quality of recommendation system and solve the existing similarity calculation inaccuracy problem of traditional collaborative filtering algorithm,this paper put forward a method to calculate user similarity.Based on the user common ratings,this method firstly calculates the information entropy of rating differentials according to rating differentials and time features.Then it evaluates the similarity of the user by utilizing the information entropy of rating differentials and the rated item attributes.Finally,the nearest neighbors would be calculated according to the user similarity,which helps predict the rating of the target item.The experimental results show that the proposed algorithm makes the target user find the nearest neighbors more accurately and improves the recommendation accuracy effectively.

Key words: Collaborative filtering, Common ratings, Item attributes, Similarity measure

中图分类号: 

  • TP311.5
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