叶晓庆,刘盾,梁德翠.基于协同过滤的三支粒推荐算法研究[J].计算机科学,2018,45(1):90-96
基于协同过滤的三支粒推荐算法研究
Three-way Granular Recommendation Algorithm Based on Collaborative Filtering
投稿时间:2017-03-03  修订日期:2017-06-09
DOI:10.11896/j.issn.1002-137X.2018.01.014
中文关键词:  协同过滤,三支决策,粒计算,三支粒推荐
英文关键词:Collaborative filtering,Three-way decision,Granular computing,three-way granular recommendation
基金项目:本文受国家自然科学基金项目(71571148,6,71201133),四川省科技厅应用基础面上项目(2017JY0220),四川省电子商务与现代物流研究中心项目(DSWL16-2),四川省留学回国人员科技活动择优资助
作者单位E-mail
叶晓庆 西南交通大学经济管理学院 成都610031 448013658@qq.com 
刘盾 西南交通大学经济管理学院 成都610031 newton83@163.com 
梁德翠 电子科技大学经济与管理学院 成都610054 decuiliang@126.com 
摘要点击次数: 542
全文下载次数: 385
中文摘要:
      为了降低传统协同过滤算法的推荐成本,并解决该算法评分信息单一的问题,提出了一种基于协同过滤的三支粒推荐算法。该算法在传统协同过滤的基础上,考虑项目特征对用户评分的影响,根据项目特征、粒化用户项目评分矩阵,形成用户对项目粒度的评分矩阵,并以此作为用户偏好的测度依据。同时,该算法在推荐过程中引入三支决策,考虑了推荐过程中产生的误分类成本和学习成本,并基于用户真实的评分偏好构建三支推荐。实验结果显示,基于协同过滤的三支粒推荐算法与传统协同过滤算法相比,不但提高了算法的推荐质量,而且降低了推荐成本。
英文摘要:
      To decrease the recommendation cost and solve the problem of single rating of traditional collaborative filtering algorithm,this paper proposed a three-way granular recommendation algorithm based on collaborative filtering.On the basis of collaborative filtering,this algorithm considers the influence of items’ characteristics on users’ ratings,and constructs user-item’s granulation rating matrix through granulating user-item rating matrix by the characteristics of items,which is used to measure users’ preferences.At the same time,this algorithm considers both misclassification cost and teacher cost during the process of recommendation,and constructs three-way recommendation based on users’ real preferences on rating.Experimental results show that compared with traditional collaborative filtering algorithm,three-way granular recommendation algorithm based on collaborative filtering not only improves the quality of the re-commendation,but also decreases the recommendation cost.
查看全文  查看/发表评论  下载PDF阅读器