基于协同过滤的电子商务推荐系统研究
|
摘要
本文研究了目前电子商务领域中普遍采用的个性化推荐系统,介绍了个性化推荐系统的国内外研究现状,重点分析了其中应用最为广泛的基于用户的协同过滤推荐系统的工作原理,并针对传统的基于用户的协同过滤推荐系统中所存在的用户评价稀疏、存储空间利用度低、系统自适应性不强等缺点,提出了一种改进的协同过滤推荐系统。
本文引入了一种改进的十字链表存储结构存储用户-资源评价矩阵中的数据元素,既可以支持矩阵的动态变化,又最大程度的压缩了系统的存储空间。同时本文将本体与语义网的理论引入到系统中,通过利用资源之间的语义关系,来预测用户没有显式评分的资源得分,在一定程度上解决了评价稀疏性所带来的推荐精度不高的问题。此外,系统将运行结果记录下来,同步刷新用户资源评价矩阵,使得系统可以充分利用上次运行结果,逐渐提高推荐精度,有效地改善了系统的自适应性。本文利用统计精度度量方法对传统的基于用户的协同过滤推荐系统与改进后的推荐系统进行了对比,证明了系统在推荐精度方面的改进。
This paper researches on the personalized recommendation systems current widely used in electronic commerce and introduces general situation of the research on personalized recommendation systems at home and abroad. The paper focuses on user-based collaborative filtering recommendation system which is the mostly used and analyzes its working principle. To aim at the existing problems such as the sparsity of user evalution, the low storage space utilization, the shortcoming of self-adaptive, this paper raises a solution as an improved collaborative filtering recommendation system.
This paper adopts an improved cross list storage structure to store data elements in user-resource evaluation matrix, which not only can support the dynamic change of the matrix, but also can compress system storage space in the greatest degree. At the same time, this paper brings the theory of Semantic Web and Ontology into collaborative filtering recommendation system, predicting the score of rescource that user hasn't given an explicit score with the help of semantic relations between resources, which solves the problem of low recommendation precision brought about by sparsity of evaluation to a certain extent. In addition, the improved system will record the results and refresh the user-resource evaluation matrix synchronously, which can take full advantage of previous results to increase recommendation precision gradually and then improves the system adaptability effectively. In this paper, statistical precision of measurement method is used to compare the traditional user-based collaborative filtering recommendation system and the improved collaborative filtering recommendation system in order to prove the improvement at system recommendation accuracy.
本文引入了一种改进的十字链表存储结构存储用户-资源评价矩阵中的数据元素,既可以支持矩阵的动态变化,又最大程度的压缩了系统的存储空间。同时本文将本体与语义网的理论引入到系统中,通过利用资源之间的语义关系,来预测用户没有显式评分的资源得分,在一定程度上解决了评价稀疏性所带来的推荐精度不高的问题。此外,系统将运行结果记录下来,同步刷新用户资源评价矩阵,使得系统可以充分利用上次运行结果,逐渐提高推荐精度,有效地改善了系统的自适应性。本文利用统计精度度量方法对传统的基于用户的协同过滤推荐系统与改进后的推荐系统进行了对比,证明了系统在推荐精度方面的改进。
This paper researches on the personalized recommendation systems current widely used in electronic commerce and introduces general situation of the research on personalized recommendation systems at home and abroad. The paper focuses on user-based collaborative filtering recommendation system which is the mostly used and analyzes its working principle. To aim at the existing problems such as the sparsity of user evalution, the low storage space utilization, the shortcoming of self-adaptive, this paper raises a solution as an improved collaborative filtering recommendation system.
This paper adopts an improved cross list storage structure to store data elements in user-resource evaluation matrix, which not only can support the dynamic change of the matrix, but also can compress system storage space in the greatest degree. At the same time, this paper brings the theory of Semantic Web and Ontology into collaborative filtering recommendation system, predicting the score of rescource that user hasn't given an explicit score with the help of semantic relations between resources, which solves the problem of low recommendation precision brought about by sparsity of evaluation to a certain extent. In addition, the improved system will record the results and refresh the user-resource evaluation matrix synchronously, which can take full advantage of previous results to increase recommendation precision gradually and then improves the system adaptability effectively. In this paper, statistical precision of measurement method is used to compare the traditional user-based collaborative filtering recommendation system and the improved collaborative filtering recommendation system in order to prove the improvement at system recommendation accuracy.
引文
[1]Konstan J,Miller B,Maltz D,Herlocker J,Gordon L.GroupLens:Applying collaborative filtering to Use-net news[J].Communications of the ACM,40(3):77-87P,2004.
[2]Resnick and Varian.Recommender systems[J].Communications of the ACM,40(3):56-58P,2006.
[3]Schafer J B,Konstan J,Riedl J.Recommender systems in E-commerce'[A].In:EC'99:Proceedings of the First ACM Conference on Electronic Commerce[C],Denver,CO,158-166P,1999.
[4]Tran T,Cohen R.Hybrid Recommender Systems for Electronic Commerce'[R].In Knowledge-Based Electronic Markets,Papers from the AAAI Workshop.AAAI Technical Report WS-00-04,78-83P,Menlo Park,CA:AAAI Press,2000.
[5]黎星星,黄小琴.朱庆生,《电子商务推荐系统研究》[J].《计算机工程与科学》,2004年第26卷第5期.
[6]邓爱林等,《基于项目评分预测的协同过滤推荐算法》[J].《软件学报》,2003,第14卷第9期,1621-1628页.
[7]赵亮,胡乃静,张守志,《个性化推荐算法设计》[J],《计算机研究与发展》,2002年,第39卷第8期,986-989页.
[8]余力,刘鲁,罗掌华,《我国电子商务推荐策略的比较分析》[J],《工程理论与实践》,2004年8月.
[9]Myra Spiliopoulou.The laborious way from data mining to the web mining.Int.Journal of Comp.Sys.Sci.Eng.Special Issue on "Semantics of the Web".1999,14:113-126P.
[10]杜小勇,李曼,王珊.本体学习研究综述[J].软件学报,2006.17(9):1837-1847页.
[11]Maedche A.,G.Neuman,S.Staab.Bootstrapping an Ontology-Based Information Extraction System.Studies in Fuzziness and Soft Computing.Intelligent Exploration of the Web,Springer,2002.
[12]Omelayenko B.Learning of ontology for the Web:the analysis of existent approaches.In:Proceedings of the International Workshop on Web Dynamics,London,2001.
[13]Bourigault,D.Surface Grammatical Analysis for the Extraction of Terminological Noun Phrase.Proceedings of COLING92,977-981P.
[14]Justeson,J.S.and Kat,S.M.Teehnlcal Terminology:Some Linguistic Properties and an Algorithm for Identification in Text.Natural Language Engineering,1995,9-27P.
[15]Patrick P.and Dekang Lin.A Statistical Corpus-Based Term Extractor.Canadian Conference on AI2001,36-46P.
[16]K.T.Frantzi,S.Ananiadou.The C-Value/NC-Value domain independent method for multi-word term extraction.Journal of Natural Language Processing,6(3):145-179P,1999.
[17]Cohen.J.D.Highlights:Language and Domain-Independent Automatic Indexing Terms for Abstracting.Journal of the American Society for Information Science,199546(3):162-174P.
[18]Paul Resnick,Hal R.Varian,Recommender systems,Communications of the ACM,1997.40(3)56-58P.
[19]Schafer,J.B.,Konstan,J.A,and Riedl,J.E-Commerce Recommendation Applications.Data Mining and Knowledge Discovery,2001.
[20]王纪辉,赵卓宁.基于协同过滤的电子商务网站个性化推荐系统设计[J].成都信息工程学院学报,2007,8.
[21]刘开南.基于兴趣的电子商务数据挖掘技术的研究[J].商场现代化,2007,1.
[22]黄光球,靳峰,彭绪友.基于兴趣度的协同过滤商品推荐系统模型[J].微电子学与计算机,2005,3期.
[23]王太雷.个性化推荐系统相似模式聚类研究[J].计算机工程,2005,5.
[24]徐小琳,朔喜戎等.信息过滤技术和个性化信息服务[J].计算机工程与应 用,2003.39(9):182-184P.
[25]李勇,徐振宁.Internet个性化信息服务研究综述[J].计算机工程与应用,2002.38(19):183-187P.
[26]刘柏篙.信息过滤研究[J].现代图书情报技术,2003(6):23页.
[27]邓爱林.电子商务推荐系统关键技术研究[J].复旦大学计算机系(博士论文),2001.
[28]欧立奇,陈莉等.协同过滤算法中新项目推荐算法的研究[J].微计算机信息,2005,11.
[291 陈晓红,沈洁等.基于用户潜在偏好的协同过滤[J].计算机工程,2007,2.
[30]吴颜,沈洁,顾天竺等.协同过滤推荐系统中数据稀疏问题的解决[J].计算机应用研究,2007,6.
[31]Robin Burke.Hybrid Recommender Systems:Survey and Experiments[EB/OL].http://josquin.cti.depaul.edu/～rburke/pubs/burke-umu ai02.pdf.
[32]Jun Wang,Arjen P,de Vries,etc.A User-Item Relevance Model for Log-based Collaborative Filtering[A].ACM[C].1997.
[33]Chien.Y.-H.,George.E.I.A Bayesian Model for Collaborative Filtering[A].Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics[C].1999.
[34]Sarwar.B,Karypis.G,Konstan etc.Item-Based collaborative filtering recommendation.Proceedings of the 10~(th) International World Wide Web Conference,2001.
[35]蔡自兴,郑金华,朱珍民.稀疏矩阵乘法运算的并行算法[J].湘潭大学自然科学学报,2000,22(1):1-3页.
[36]安学庆,秦体恒,李学相.BREGMAN行处理算法的松弛改进[J].河南科学,2000,18(3):312-319页.
[37]陈文伟.决策支持系统教程[M].北京:清华大学出版社,2004.
[38]Geoffrion.An Introduction to Structured Modeling[J].Management Science,1987,33(5):547-583P.
[39]NavigliR,Velardi P,Gangemi A.Ontology learning and its application to automated terminology translation.IEEE Intelligent Systems,2003,18(1):22-30P.
[40]K.T.Frantzi,S.Ananiadou.The C-Value/NC-Value domain independent method for multi-word term extraction.Journal of Natural Language Processing,6(3):145-179P,1999.
[41]Shamsfard M.,Barforoush A.A.Learning ontologies from natural language texts.International Journal of Human Computer Studies,2004,60(1):17-63P.
[42]张锋,许云,侯艳,樊孝忠.基于互信息的中文术语抽取系统[J].计算机应用研究,2005(5).
[43]Paul Buitelaar.Philipp Cimiano.Ontology Learning from Text.Tutorial at EACL,April 2006,Trento,Italy.
[44]Maedche A.Ontology learning for the Semantic Web[M].Boston:Kluwer Academic,2002.
[45]张华平,刘群.基于N-最短路径方法的中文词语粗切分模型[J].中文信息学报,2002,16(5):1-7页.
[2]Resnick and Varian.Recommender systems[J].Communications of the ACM,40(3):56-58P,2006.
[3]Schafer J B,Konstan J,Riedl J.Recommender systems in E-commerce'[A].In:EC'99:Proceedings of the First ACM Conference on Electronic Commerce[C],Denver,CO,158-166P,1999.
[4]Tran T,Cohen R.Hybrid Recommender Systems for Electronic Commerce'[R].In Knowledge-Based Electronic Markets,Papers from the AAAI Workshop.AAAI Technical Report WS-00-04,78-83P,Menlo Park,CA:AAAI Press,2000.
[5]黎星星,黄小琴.朱庆生,《电子商务推荐系统研究》[J].《计算机工程与科学》,2004年第26卷第5期.
[6]邓爱林等,《基于项目评分预测的协同过滤推荐算法》[J].《软件学报》,2003,第14卷第9期,1621-1628页.
[7]赵亮,胡乃静,张守志,《个性化推荐算法设计》[J],《计算机研究与发展》,2002年,第39卷第8期,986-989页.
[8]余力,刘鲁,罗掌华,《我国电子商务推荐策略的比较分析》[J],《工程理论与实践》,2004年8月.
[9]Myra Spiliopoulou.The laborious way from data mining to the web mining.Int.Journal of Comp.Sys.Sci.Eng.Special Issue on "Semantics of the Web".1999,14:113-126P.
[10]杜小勇,李曼,王珊.本体学习研究综述[J].软件学报,2006.17(9):1837-1847页.
[11]Maedche A.,G.Neuman,S.Staab.Bootstrapping an Ontology-Based Information Extraction System.Studies in Fuzziness and Soft Computing.Intelligent Exploration of the Web,Springer,2002.
[12]Omelayenko B.Learning of ontology for the Web:the analysis of existent approaches.In:Proceedings of the International Workshop on Web Dynamics,London,2001.
[13]Bourigault,D.Surface Grammatical Analysis for the Extraction of Terminological Noun Phrase.Proceedings of COLING92,977-981P.
[14]Justeson,J.S.and Kat,S.M.Teehnlcal Terminology:Some Linguistic Properties and an Algorithm for Identification in Text.Natural Language Engineering,1995,9-27P.
[15]Patrick P.and Dekang Lin.A Statistical Corpus-Based Term Extractor.Canadian Conference on AI2001,36-46P.
[16]K.T.Frantzi,S.Ananiadou.The C-Value/NC-Value domain independent method for multi-word term extraction.Journal of Natural Language Processing,6(3):145-179P,1999.
[17]Cohen.J.D.Highlights:Language and Domain-Independent Automatic Indexing Terms for Abstracting.Journal of the American Society for Information Science,199546(3):162-174P.
[18]Paul Resnick,Hal R.Varian,Recommender systems,Communications of the ACM,1997.40(3)56-58P.
[19]Schafer,J.B.,Konstan,J.A,and Riedl,J.E-Commerce Recommendation Applications.Data Mining and Knowledge Discovery,2001.
[20]王纪辉,赵卓宁.基于协同过滤的电子商务网站个性化推荐系统设计[J].成都信息工程学院学报,2007,8.
[21]刘开南.基于兴趣的电子商务数据挖掘技术的研究[J].商场现代化,2007,1.
[22]黄光球,靳峰,彭绪友.基于兴趣度的协同过滤商品推荐系统模型[J].微电子学与计算机,2005,3期.
[23]王太雷.个性化推荐系统相似模式聚类研究[J].计算机工程,2005,5.
[24]徐小琳,朔喜戎等.信息过滤技术和个性化信息服务[J].计算机工程与应 用,2003.39(9):182-184P.
[25]李勇,徐振宁.Internet个性化信息服务研究综述[J].计算机工程与应用,2002.38(19):183-187P.
[26]刘柏篙.信息过滤研究[J].现代图书情报技术,2003(6):23页.
[27]邓爱林.电子商务推荐系统关键技术研究[J].复旦大学计算机系(博士论文),2001.
[28]欧立奇,陈莉等.协同过滤算法中新项目推荐算法的研究[J].微计算机信息,2005,11.
[291 陈晓红,沈洁等.基于用户潜在偏好的协同过滤[J].计算机工程,2007,2.
[30]吴颜,沈洁,顾天竺等.协同过滤推荐系统中数据稀疏问题的解决[J].计算机应用研究,2007,6.
[31]Robin Burke.Hybrid Recommender Systems:Survey and Experiments[EB/OL].http://josquin.cti.depaul.edu/～rburke/pubs/burke-umu ai02.pdf.
[32]Jun Wang,Arjen P,de Vries,etc.A User-Item Relevance Model for Log-based Collaborative Filtering[A].ACM[C].1997.
[33]Chien.Y.-H.,George.E.I.A Bayesian Model for Collaborative Filtering[A].Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics[C].1999.
[34]Sarwar.B,Karypis.G,Konstan etc.Item-Based collaborative filtering recommendation.Proceedings of the 10~(th) International World Wide Web Conference,2001.
[35]蔡自兴,郑金华,朱珍民.稀疏矩阵乘法运算的并行算法[J].湘潭大学自然科学学报,2000,22(1):1-3页.
[36]安学庆,秦体恒,李学相.BREGMAN行处理算法的松弛改进[J].河南科学,2000,18(3):312-319页.
[37]陈文伟.决策支持系统教程[M].北京:清华大学出版社,2004.
[38]Geoffrion.An Introduction to Structured Modeling[J].Management Science,1987,33(5):547-583P.
[39]NavigliR,Velardi P,Gangemi A.Ontology learning and its application to automated terminology translation.IEEE Intelligent Systems,2003,18(1):22-30P.
[40]K.T.Frantzi,S.Ananiadou.The C-Value/NC-Value domain independent method for multi-word term extraction.Journal of Natural Language Processing,6(3):145-179P,1999.
[41]Shamsfard M.,Barforoush A.A.Learning ontologies from natural language texts.International Journal of Human Computer Studies,2004,60(1):17-63P.
[42]张锋,许云,侯艳,樊孝忠.基于互信息的中文术语抽取系统[J].计算机应用研究,2005(5).
[43]Paul Buitelaar.Philipp Cimiano.Ontology Learning from Text.Tutorial at EACL,April 2006,Trento,Italy.
[44]Maedche A.Ontology learning for the Semantic Web[M].Boston:Kluwer Academic,2002.
[45]张华平,刘群.基于N-最短路径方法的中文词语粗切分模型[J].中文信息学报,2002,16(5):1-7页.