Mining Evolving Association Rules for E-Business Recommendation

doi:10.1007/s12204-012-1246-4

上海交通大学学报（英文版） ›› 2012, Vol. 17 ›› Issue (2): 161-165.doi: 10.1007/s12204-012-1246-4

Mining Evolving Association Rules for E-Business Recommendation

LONG Shun (龙舜), ZHU Wei-heng^∗(朱蔚恒)

(Department of Computer Science, Jinan University, Guangzhou 510632, China; Emergency Technology Research Center of Risk Evaluation and Prewarning on Public Network Security, Guangzhou 510632, China)

出版日期:2012-04-28 发布日期:2012-05-31
通讯作者: ZHU Wei-heng(朱蔚恒) E-mail: tzhuwh@jnu.edu.cn

Mining Evolving Association Rules for E-Business Recommendation

LONG Shun (龙舜), ZHU Wei-heng^∗(朱蔚恒)

(Department of Computer Science, Jinan University, Guangzhou 510632, China; Emergency Technology Research Center of Risk Evaluation and Prewarning on Public Network Security, Guangzhou 510632, China)

Online:2012-04-28 Published:2012-05-31
Contact: ZHU Wei-heng? (朱蔚恒) E-mail: tzhuwh@jnu.edu.cn

摘要/Abstract

摘要： Association analysis is an effective data mining approach capable of unveiling interesting associations within a large dataset. Although widely adopted in e-business areas, it still has many difficulties when applied in practice. For instance, there is a mismatch between the static rules discovered and the drifting nature of the user interests, and it is difficult to detect associations from a huge volume of raw user data. This paper presents an effective approach to mine evolving association rules in order to tackle these problems. It is followed by a recommendation model based on the evolving association rules unveiled. Experimental results on an online toggery show that it can effectively unveil people’s shifting interests and make better recommendations accordingly.

关键词: data mining, evolving association rules, personalized recommendation

Abstract: Association analysis is an effective data mining approach capable of unveiling interesting associations within a large dataset. Although widely adopted in e-business areas, it still has many difficulties when applied in practice. For instance, there is a mismatch between the static rules discovered and the drifting nature of the user interests, and it is difficult to detect associations from a huge volume of raw user data. This paper presents an effective approach to mine evolving association rules in order to tackle these problems. It is followed by a recommendation model based on the evolving association rules unveiled. Experimental results on an online toggery show that it can effectively unveil people’s shifting interests and make better recommendations accordingly.

Key words: data mining, evolving association rules, personalized recommendation

中图分类号:

TP 181

LONG Shun (龙舜), ZHU Wei-heng (朱蔚恒). Mining Evolving Association Rules for E-Business Recommendation[J]. 上海交通大学学报（英文版）, 2012, 17(2): 161-165.

LONG Shun (龙舜), ZHU Wei-heng (朱蔚恒). Mining Evolving Association Rules for E-Business Recommendation[J]. Journal of shanghai Jiaotong University (Science), 2012, 17(2): 161-165.

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Mining Evolving Association Rules for E-Business Recommendation

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