[1]叶锡君,袁培森,郭小清,等.基于用户兴趣和项目周期的协同过滤推荐算法[J].南京理工大学学报(自然科学版),2018,42(04):392.[doi:10.14177/j.cnki.32-1397n.2018.42.04.002]
 Ye Xijun,Yuan Peisen,Gou Xiaoqing,et al.Collaborative filtering recommendation algorithm based onuser interest and project cycle[J].Journal of Nanjing University of Science and Technology,2018,42(04):392.[doi:10.14177/j.cnki.32-1397n.2018.42.04.002]
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基于用户兴趣和项目周期的协同过滤推荐算法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年04期
页码:
392
栏目:
出版日期:
2018-08-30

文章信息/Info

Title:
Collaborative filtering recommendation algorithm based onuser interest and project cycle
文章编号:
1005-9830(2018)04-0392-09
作者:
叶锡君袁培森郭小清闫智慧何 婧
南京农业大学 信息科学技术学院,江苏 南京 210095
Author(s):
Ye XijunYuan PeisenGou XiaoqingYan ZhihuiHe Jing
School of Information Sciences and Technology,Nanjing Agricultural University,Nanjing 210095,China
关键词:
推荐算法 协同过滤 个性化 用户兴趣 项目周期 用户相似度 项目相似度 线性融合
Keywords:
recommendation algorithm collaborative filtering individuation user interest project time user similarity item similarity linear fusion
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.04.002
摘要:
协同过滤推荐算法以没有限定推荐对象类型、无需用户反馈信息等优势在众多个性化推荐算法中脱颖而出。但是现有算法缺乏对用户之间的差异和用户自身的兴趣考虑,对用户和项目之间的潜在关联考虑不充分,这些问题均会影响推荐精度。该文提出一种基于用户兴趣和项目周期的协同过滤推荐算法,该算法在计算相似度时引入用户兴趣权重UI、项目时间等因素,并采用融合因子将改进后所得用户和项目信息进行综合,获得推荐列表。对比实验得出:该算法在推荐精确度上提高了11.034%,研究结果表明:该算法可有效提高推荐精确度。
Abstract:
Collaborative filtering recommendation algorithm takes the advantage of non-restricting recommended object type,without-user feedback information and so on,and stands out in many personalized recommendation algorithms. However,the existing algorithms don’t consider the differences between users and user interests,and the potential association between users and projects yet. These problems all affect the recommendation accuracy. This paper proposes the collaborative filtering recommendation algorithm based on user interest and project cycle,which introduces user interest weight UI,project time and other factors in the computation of similarity. This paper uses the fusion factor to integrate the improved user and project information to get the recommendation list. Finally,a comparison experiment shows that the proposed algorithm improves the recommendation accuracy by 11.034%. The research results show that the algorithm can effectively improve the accuracy of recommendation.

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备注/Memo

备注/Memo:
收稿日期:2018-05-29 修回日期:2018-07-07
基金项目:国家自然科学基金(61502236); 国家重点研发计划重点专项(2016YFD0300607); 中央高校基本科研业务费专项资金(KYZ200919)
作者简介:叶锡君(1964-),男,博士,副教授,主要研究方向:数据挖掘、计算机网络、生物信息学,E-mail:yexj@njau.edu.cn;
通讯作者:何婧(1990-),女,硕士生,主要研究方向:数据挖掘,E-mail:hejing0518@163.com。
引文格式:叶锡君,袁培森,郭小清,等. 基于用户兴趣和项目周期的协同过滤推荐算法[J]. 南京理工大学学报,2018,42(4):392-400. 投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-08-30