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。
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.
作者
魏慧娟
戴牡红
WEI Hui-juan ,DAI Mu- hong(College of Information Science and Engineering, Hunan University,Changsha 410082 ,Chin)
出处
《计算机科学》
CSCD
北大核心
2018年第B06期398-401,422,共5页
Computer Science
关键词
协同过滤
相似性计算
共同评分
项目属性
Collaborative filtering
Similarity measure
Common ratings
Item attributes
