利用动态产品分类树改进的关联规则推荐方法
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摘要
针对关联规则过于稀疏导致的弱关联规则问题,以及关联规则推荐存在的多样性匮乏等问题,提出基于Vague理论生成动态产品分类树,在分类树内实施关联规则挖掘以解决弱关联规则问题;在此基础上进一步提出一种基于产品相似性的多样性选择算法,并在推荐结果集内实施多样性选择以解决推荐多样性问题,实验评价结果表明该方法与传统推荐方法相比,无论在推荐精度还是推荐多样性上都更为有效。
According to the weak association rule problems which caused by sparse association rule,and recommendation problems such as the lack of diversity,the dynamic product taxonomy is generated based on Vague set theory,and mining association rule in product taxonomy is presented to solve the weak association rules problem; on this basis,a diversity selection algorithm is proposed to solve recommendation diversity problem in recommendation result set. Experimental evaluation results show that both in the recommendation accuracy and recommendation diversity is more effective compared with the traditional recommendation method.
According to the weak association rule problems which caused by sparse association rule,and recommendation problems such as the lack of diversity,the dynamic product taxonomy is generated based on Vague set theory,and mining association rule in product taxonomy is presented to solve the weak association rules problem; on this basis,a diversity selection algorithm is proposed to solve recommendation diversity problem in recommendation result set. Experimental evaluation results show that both in the recommendation accuracy and recommendation diversity is more effective compared with the traditional recommendation method.
引文
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[12]朱郁筱,吕琳媛.推荐系统评价指标综述[J].电子科技大学学报,2012,41(2):163-175.
[13]Kant V.Enhancing recommendation quality of content-based filtering through collaborative predictions and fuzzy similarity measures[C]//International Conference on Modeling,Optimization and Computing(ICMOC 2013),Procedia Engineering,2013,38:939-944.
[14]Pathak B,Garfinkel R,Gopal R D,et al.Empirical analysis of the impact of recommender systems on sales[J].Journal of Management Information Systems,2010,27(2):159-188.
[2]赵艳霞,梁昌勇.基于关联规则的推荐系统在电子商务中的应用[J].价值工程,2006(5):82-86.
[3]崔春生,李光.基于Vague集的电子商务推荐系统研究[J].计算机工程与应用,2012,47(10):237-239.
[4]王伟平.基于Vague集的语言型多准则决策的研究[D].北京:北京理工大学,2008.
[5]Agrawal R S R.Fast algorithms for mining association rules[C]//Proc 20th Int Conf Very Large Data Bases,VLDB,Santiago,1994.
[6]贾冬艳.基于双重邻居选取策略的协同过滤推荐算法[J].计算机研究与发展,2013,50(5):1076-1084.
[7]许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362.
[8]Hung L P.A personalized recommendation system based on product taxonomy for one-to-one marketing online[J].Expert Systems with Applications,2005,29:383-392.
[9]Cho Y H,Kim J K.Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce[J].Expert Systems with Applications,2004,26:233-246.
[10]张慧颖,薛福亮.一种集成客户终身价值与协同过滤的推荐方法[J].现代图书情报技术,2012,28(1):46-52.
[11]张富国,徐升华.基于信任的电子商务推荐多样性研究[J].情报学报,2010(2):350-356.
[12]朱郁筱,吕琳媛.推荐系统评价指标综述[J].电子科技大学学报,2012,41(2):163-175.
[13]Kant V.Enhancing recommendation quality of content-based filtering through collaborative predictions and fuzzy similarity measures[C]//International Conference on Modeling,Optimization and Computing(ICMOC 2013),Procedia Engineering,2013,38:939-944.
[14]Pathak B,Garfinkel R,Gopal R D,et al.Empirical analysis of the impact of recommender systems on sales[J].Journal of Management Information Systems,2010,27(2):159-188.