通信学报2017，Vol.38Issue(10)：18-25,8.DOI:10.11959/j.issn.1000-436x.2017160
支持推荐非空率的关联规则推荐算法
Association rules recommendation algorithm supporting recommendation nonempty
摘要
Abstract
Existing association rule recommendation technologies were focus on extraction efficiency of association rule in data mining. However, it lacked consideration of recommendation balance between popular and unusual data and effi-cient processing. In order to improve the quality and efficiency of personalized recommendation and balance the recom-mendation weight of cold and hot data, the problem of mining frequent itemset based on association rule was revaluated and analyzed, a new evaluation metric called recommendation RecNon and a notion of k-pre association rule were defined, and the pruning strategy based on k-pre frequent itemset was designed. Moreover, an association rule mining algorithm based on the idea was proposed, which optimized the Apriori algorithm and was suitable for different evaluation criteria, reduced the time complexity of mining frequent itemset. The theoretic analysis and experiment results on the algorithm show that the method improved the efficiency of data mining and has higher RecNon, F-measure and precision of rec-ommendation, and efficiently balance the recommendation weight of cold data and popular one.关键词
关联规则/推荐系统/推荐非空率/数据挖掘Key words
association rule/recommender system/recommendation nonempty/data mining分类
信息技术与安全科学引用本文复制引用
何明,刘伟世,张江..支持推荐非空率的关联规则推荐算法[J].通信学报,2017,38(10):18-25,8.基金项目
国家自然科学基金资助项目(No.91646201, No.91546111) （No.91646201, No.91546111）
北京市自然科学基金资助项目(No.4153058, No.4113076) （No.4153058, No.4113076）
北京市教委面上基金资助项目(No.KM201710005023) The National Natural Science Foundation of China (No.91646201, No.91546111), The Natural Science Founda-tion of Beijing (No.4153058, No.4113076), General Project of Beijing Municipal Education Commission (No.KM201710005023) （No.KM201710005023）
