结合时间上下文挖掘学习兴趣的协同过滤推荐算法

doi:10.13190/j.jbupt.2014.06.010

北京邮电大学学报 ›› 2014, Vol. 37 ›› Issue (6): 49-53.doi: 10.13190/j.jbupt.2014.06.010

结合时间上下文挖掘学习兴趣的协同过滤推荐算法

鄂海红, 宋美娜, 李川, 江周峰

北京邮电大学计算机学院, 北京 100876

收稿日期:2014-04-25 出版日期:2014-12-28 发布日期:2014-10-17
作者简介:鄂海红(1982-),女,讲师,E-mail:ehaihong@bupt.edu.cn.
基金资助:
高等学校博士学科点专项科研基金项目(20110005120007); 北京高等学校青年英才计划项目(YETP0445); 北京市教育委员会共建项目专项资助项目

A Collaborative Filtering Recommendation Algorithm with Time Context for Learning Interest Mining

E Hai-hong, SONG Mei-na, LI Chuan, JIANG Zhou-feng

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2014-04-25 Online:2014-12-28 Published:2014-10-17

摘要/Abstract

摘要：

提出了一种基于时间上下文的协同过滤推荐(TCCF-LI)算法,实现了基于高校图书馆图书借阅记录数据上的学生学习兴趣挖掘.在传统协同过滤算法上引入时间上下文信息,既考虑了大尺度用户群体爱好的趋同性,又兼顾了小尺度个体用户爱好的短时相关性,获得了更高的推荐性能.在实际数据集上的实验结果表明,该算法在推荐精准度、召回率等方面比传统推荐算法有较好表现.

关键词: 推荐系统, 协同过滤, 时间上下文, 学习兴趣挖掘

Abstract:

A time context based collaborative filtering recommendation (TCCF-LI) algorithm this paper was proposed, and students' learning interest mining from university library borrow record was implemented. The time context information was imported into the traditional collaborative filtering recommendation algorithm, in which, both interest homoplasy of large scale user groups and short-term correlation of small scale user groups was considered. Good recommendation performance was gained. According to the experiments on real dataset, TCCF-LI algorithm presents higher precision and recall rate compared with traditional recommendation algorithm.

Key words: recommender system, collaborative filtering, time context, learning interest mining

中图分类号:

TP311

鄂海红, 宋美娜, 李川, 江周峰. 结合时间上下文挖掘学习兴趣的协同过滤推荐算法[J]. 北京邮电大学学报, 2014, 37(6): 49-53.

E Hai-hong, SONG Mei-na, LI Chuan, JIANG Zhou-feng. A Collaborative Filtering Recommendation Algorithm with Time Context for Learning Interest Mining[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(6): 49-53.

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结合时间上下文挖掘学习兴趣的协同过滤推荐算法

A Collaborative Filtering Recommendation Algorithm with Time Context for Learning Interest Mining

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