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New Technology of Library and Information Service  2007, Vol. 2 Issue (10): 57-61    DOI: 10.11925/infotech.1003-3513.2007.10.13
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Related Document Recommending Based on Weighted Association Rule Mining
Chen Zuqin1   Zhang Huiling2   Ge Jike1   Zheng Hong1
1(Faculty of Computer and Information Science, Southwest University, Chongqing 400715，China)
2(Southwest University Library,Chongqing 400715,China)
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Abstract  

This paper uses association rule mining algorithm to analyze the database and to recommend related document. Meanwhile, this paper proposes mixed weighted association rules that fit to recommend related document. This paper identifies the related document and the vertical weight by analyzing users’ behavior.The authors use the Google’s PageRank algorithm to define the documents’ horizontal weight and obtain some meaningful results.
Key words： Data mining      Association rule      Weighted association rule      Algorithm      Related document recommending     
Received: 27 July 2007      Published: 25 October 2007
: 

TP391

 		 
Corresponding Authors: Chen Zuqin     E-mail: daodao@swu.edu.cn
About author:: Chen Zuqin,Zhang Huiling,Ge Jike,Zheng Hong
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Cite this article:

Chen Zuqin,Zhang Huiling,Ge Jike,Zheng Hong. Related Document Recommending Based on Weighted Association Rule Mining. New Technology of Library and Information Service, 2007, 2(10): 57-61.

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http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2007.10.13     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2007/V2/I10/57

［1］ Zhang C Q, Zhang S C. Association Rule Mining-Models and Algorithms-Introduction［M］. Lecture Notes in Computer Science Springer.第1版，2002.
［2］ Agrawal R，Srikant R. Fast Algorithm for Mining Association Rules［C］.In：Proceedings of the International Conference on Very Large Data Bases. Santiago，Chile，1994：487-499.
［3］ 路松峰，胡和平.加权关联规则的开采［J］.小型微型计算机系统,2001，22（3）：347-350.
［4］ 欧阳为民，郑诚，蔡庆生.数据库中加权关联规则的发现［J］.软件学报，2001，12（4）：612-619.
［5］ 郭岩，白硕，杨志峰.网络日志规模分析和用户兴趣挖掘［J］.计算机学报，2005，28（9）:1483-1496.
［6］ 李长玲，翟雪梅. 基于PageRank的引文分析方法探讨［J］.情报理论与实践，2007，30（1）：122-124.
［7］ 吴淑燕，许涛.PageRank算法的原理简介［J］.图书情报工作，2003（2）：55-60，51.
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