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一种具有最大推荐非空率的关联规则挖掘方法
引用本文:王大玲,于戈,鲍玉斌. 一种具有最大推荐非空率的关联规则挖掘方法[J]. 软件学报, 2004, 15(8): 1182-1188
作者姓名:王大玲  于戈  鲍玉斌
作者单位:东北大学,信息科学与工程学院,辽宁,沈阳,110004
基金项目:Supported by the National Natural Science Foundation of China under Grant No.60173051 (国家自然科学基金)
摘    要:为了提高个性化推荐的质量,简化推荐规则生成过程中相关参数的设置,讨论了应用于个性化推荐中的关联规则的性质,定义了"推荐非空率"这一新的推荐测度以及"1-支持频繁项集"和"k最大关联规则"的概念,提出了"在1-支持频繁项集中生成k最大关联规则"的思想,设计了满足该思想且适合于不同滑动窗口深度下推荐的关联规则挖掘算法.理论分析及实验结果表明,该算法具有最大的推荐非空率、较高的推荐准确率和F-测度,并有效地简化了规则挖掘过程中阈值的设置.

关 键 词:1-支持  关联规则  Web使用挖掘  个性化  非空率
文章编号:1000-9825/2004/15(08)1182
收稿时间:2003-04-11
修稿时间:2004-01-06

An Approach of Association Rules Mining with Maximal Nonblank for Recommendation
WANG Da-Ling,YU Ge and BAO Yu-Bin. An Approach of Association Rules Mining with Maximal Nonblank for Recommendation[J]. Journal of Software, 2004, 15(8): 1182-1188
Authors:WANG Da-Ling  YU Ge  BAO Yu-Bin
Abstract:To improve quality of personalized recommendation and simplify the preference setup in generating recommendation rules, the characteristics of the association rule for personalized recommendation are discussed, the concepts of recommendation nonblank metric, a new recommendation metric, 1-support frequent itemset and k-maximal association rule are defined, and the idea of getting k-maximal association rule from 1-support frequent itemset is proposed. Moreover, an association rule mining algorithm based on the idea is designed, which is suitable for different sliding window depths. The theoretic analysis and experiment results on the algorithm show that the method has maximal nonblank, higher precision and F-measure of recommendation, and simplifies the preference setup of thresholds in mining rules effectively.
Keywords:support  association rule  Web usage mining  personalization  nonblank
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