Association rules recommendation algorithm supporting recommendation nonempty

Ming HE; Wei-shi LIU; Jiang ZHANG

doi:10.11959/j.issn.1000-436x.2017160

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Association rules recommendation algorithm supporting recommendation nonempty

Papers | 更新时间：2024-06-05

- Association rules recommendation algorithm supporting recommendation nonempty
- Journal on Communications Vol. 38, Issue 10, Pages: 18-25(2017)
- 作者机构：
  
  1. 北京工业大学信息学部计算机学院，北京 100124
  2. 国网英大国际控股集团有限公司信息化工作部，北京 100005
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2017160
  CLC： TP319
- Online First：2017-10，
  
  Published：25 October 2017
- 稿件说明：
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Ming HE, Wei-shi LIU, Jiang ZHANG. Association rules recommendation algorithm supporting recommendation nonempty[J]. Journal on Communications, 2017, 38(10): 18-25.
DOI：

Ming HE, Wei-shi LIU, Jiang ZHANG. Association rules recommendation algorithm supporting recommendation nonempty[J]. Journal on Communications, 2017, 38(10): 18-25. DOI： 10.11959/j.issn.1000-436x.2017160.

摘要

现有的关联规则推荐技术在数据提取时主要侧重于关联规则的提取效率，缺乏对冷、热门数据推荐平衡性的考虑和有效处理。为了提高个性化推荐效率和推荐质量，平衡冷门与热门数据推荐权重，对关联规则的Apriori算法频繁项集挖掘问题进行了重新评估和分析，定义了新的测评指标推荐非空率以及k前项频繁项集关联规则的概念，设计了基于 k 前项频繁项集的剪枝方法，提出了优化 Apriori 算法且适合不同测评标准值的 k前项频繁项集挖掘算法，降低频繁项集提取的时间复杂度。理论分析比较与实验表明，k 前项剪枝方法提高了频繁项集的提取效率，拥有较高的推荐非空率、调和平均值和推荐准确率，有效地平衡了冷、热门数据的推荐权重。

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 efficient processing.In order to improve the quality and efficiency of personalized recommendation and balance the recommendation 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 recommendation

and efficiently balance the recommendation weight of cold data and popular one.

关键词

Keywords

references

ADOMAVICIUS G , TUZHILIN A . Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions [J ] . IEEE Transactions on Knowledge and Data Engineering , 2005 , 17 ( 6 ): 734 - 749 .

LIN W , ALVAREZ S , RUIZ C . Efficient adaptive-support association rule mining for recommender systems [J ] . Data Mining and Knowledge Discovery , 2002 , 6 ( 1 ): 83 - 105 .

AGRAWAL R , IMIELINSKI T , SWAMI A . Mining association rules between sets of items in large databases [C ] // The 1993 ACM SIGMOD International Conference on Management of Data . Washington , 1993 : 207 - 216 .

PARK J S , CHEN M S , YU P S . An effective hash-based algorithm for mining association rules [J ] . ACM SIGMOD Record , 1997 , 24 ( 2 ): 175 - 186 .

TOIVONEN H , . Sampling large databases of association rules [C ] // The 22th International Conference on Very Large Data Bases,San Francisco:Morgan Kaufmann Publishers Inc , 1996 : 134 - 145 .

魏玲 , 魏永江 , 高长元 , 等 . 基于Bigtable与MapReduce的Apriori算法改进 [J ] . 计算机科学 , 2015 , 42 ( 10 ): 208 - 210 .

WEI L , WEI Y J , GAO C Y , et al . Improved Apriori algorithm based on Bigtable and MapReduce [J ] . Computer Science , 2015 , 42 ( 10 ): 208 - 210 .

刘兴彬 , 杨建华 , 谢高岗 , 等 . 基于Apriori算法的流量识别特征自动提取方法 [J ] . 通信学报 , 2008 , 29 ( 12 ): 51 - 59 .

LIU X B , YANG J H , XIE G G , et al . Automated mining of packet signatures for traffic identification at application layer with Apriori algorithm [J ] . Journal on Communications , 2008 , 29 ( 12 ): 51 - 59 .

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 .

WANG D L , YU G , BAO Y B . An approach of association rules mining with maximal nonblank for recommendation [J ] . Journal of Software , 2004 , 15 ( 8 ): 1182 - 1188 .

SANDVIG J , MOBASHER B , BURKE R . Robustness of collaborative recommendation based on association rule mining [C ] // The 2007 ACM Conference on Recommender Systems . 2007 : 105 - 112 .

HONG J Y , SUH E H , KIM J , et al . Context-aware system for proactive personalized service based on context history [J ] . Expert Systems with Applications , 2009 , 36 ( 4 ): 7448 - 7457 .

CHEN W Y , CHU J C , LUAN J , et al . Collaborative filtering for orkut communities:discovery of user latent behavior [C ] // International Conference on World Wide Web . 2009 : 681 - 690 .

GARCIA E , ROMERO C , VENTURA S , et al . A collaborative educational association rule mining tool [J ] . The Internet and Higher Education , 2011 , 14 ( 2 ): 77 - 88 .

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