摘要
随着Internet的不断发展,电子商务系统给商家和客户带来了越来越多的信息,如何及时地在网上的海量信息中发现所需要的信息变得越来越困难。于是电子商务诸多的推荐系统应运而生,推荐技术成为一个研究的热点,引起人们的广泛关注。
近年来,电子商务推荐技术在理论和实践中均得到了较快的发展,与此同时,电子商务推荐系统面临着严峻挑战。针对现有电子商务推荐系统存在的问题,本文在电子商务推荐系统中引入领域本体,对电子商务推荐系统中的推荐算法及推荐模型等关键技术进行了深入的研究,以期通过引入领域本体和Web挖掘提高电子商务推荐的准确率和实时性。其主要工作与创新体现在:
(1)在探讨和分析比较各种本体构建方法基础上,借鉴软件工程学中的基于软件生命周期模型的方法论,并利用现有的本体构建工具,提出了一种新的基于原型迭代的领域本体构建方法,并构建个性化推荐所需的领域本体。
(2)稀疏性问题是协同过滤推荐所面临的最重要问题之一。针对用户评分数据的稀疏性问题,本文提出一个基于领域本体和用户偏好变化的协同过滤推荐算法。利用领域本体中项目的类型及属性计算项目之间的语义相似度,采用KNN(K Nearest Neighbor,K最近邻居)的思想根据用户对项目的评分,预测用户未评分项目的评分,填充用户评分矩阵的缺失值,而后在填充后的用户-项目评分矩阵基础上进行推荐。利用用户的特征因素对用户进行聚类,缩小最近邻居的选择范围。本算法还考虑到用户偏好的变化,引入遗忘函数,根据评价时间调整评分权重。实验结果表明:所提出的算法能够有效地解决稀疏性问题,改善了推荐的质量。
(3)传统的Web使用挖掘在个性化推荐过程中没有考虑相关领域的语义知识,不能利用对象的语义进行推荐,从而导致推荐的准确率比较低。针对上述问题,提出一种基于领域本体和Web使用挖掘的个性化推荐模型,将领域本体集成到Web挖掘和个性化推荐中。针对这一模型,本文提出一种基于语义聚类的个性化推荐算法,利用领域本体对Web数据进行预处理,并采用K-Means层次凝聚算法对交易事务进行聚类分析。而后利用各个聚类的质心点矢量来表征每个聚类,生成准确的用户访问偏好和推荐集。
(4)提出了多模型的电子商务推荐系统模型,该模型支持非个性化、协同过滤和基于Web使用挖掘的多种推荐。通过挖掘Web使用数据和用户项目-评分数据,分析用户属性信息和用户评分记录等信息,挖掘用户潜在兴趣偏好,在不断的学习中为用户提供准确实时的个性化推荐服务。以此模型为基础,设计并实现了基于电影领域本体的个性化推荐原型系统,验证了其正确性。
With the popularization of the Internet, E-Commerce systems bring more and more information for businesses and customers, and it becomes much more difficult for consumers to find services they want in a timely manner from the massive online sources. To address this issue, a variety of recommendation systems were proposed and great attentions have been paid on this new technology, which has become a hotspot in recent researches.
Although the recommendation systems in E-Commerce have been very successful in both research and practice, challenging problems still remain. Aimed at solving the main challenges of recommendation systems in E-Commerce, this dissertation attempts to integrate domain ontology with Web usage mining for recommendation personalization and gives a rewarding research on recommendation algorithms and related models in E-Commerce recommendation systems in order to improve the accuracy and the instantaneity of the systems. The main research work and innovative points discussed in this dissertation are as follows:
(1) Based on analyzing different means of domain ontology construction, a new method is proposed to construct domain ontology for personalized recommendation.
(2) Sparsity is one of the most important issues in collaborative filtering recommendation. To deal with the sparisty of user-item rating, a new collaborative filtering recommendation algorithm which is based on domain ontology and interest drift has been developed.In the new algorithm, the semantic similarity between types and values can be computed according to the domain ontology, the predicting rating of items unfilled by users can be predicted with the semantic similarity and filled according to the K Nearest Neighbors(KNN), then the recommendations can be made using the filled user-item rating matrix. Besides, the user's features can be used for user clustering to reduce the selection scope of nearest neighbors. Taking the changes in users' preferences into account, a forgetfulness function f(t) is introduced to adjust the importance of rating considering the rating time. The experiment results show that the new algorithm can solve the sparse problem effectively and has better recommendation quality than the traditional algorithm.
(3) Without using the semantics of objects, the traditional Web usage mining in personalized recommendation does not consider relative semantic knowledge, so the accuracy of recommendation is low. In order to solve this problem, a personalized recommendation model which integrating domain ontology with Web usage mining for personalization is presented. An innovative personalized recommendation algorithm based on semantic clustering is devised in this model. An ontology-based vector space model is setting up after the preprocessing on Web usage data with domain ontology. The transaction data are clustered with K-Means Agglomerative Nesting algorithm. The cancroids of clusters can be used to generate user preference and recommendation data sets.
(4) A new multiple recommendation prototype system is designed and implemented. The new system can support different multiple recommendation models such as non-personalized recommendation, personalized recommendation for registered users and personalized recommendation for unregistered users. By mining on the Web usage data and user item-rating data, analyzing the user attribute information and user rating records, the model learns the potential interests of the users, and provides instant and accurate personalized recommendation services. The effectiveness of the model is verified through the development of a personalized recommendation prototype system based on a film domain ontology.
引文
[1]Schafer,J.B.,Konstan,J.A.and Riedl,J.E-Commerce Recommendation Application[J].Data Mining and Knowledge Discovery.2001,5(1-2):115-153.
[2]Schafer,J.B.,Konstan,J.A.and Riedl,J.Recommender Systems in E-Commerce[C].ACM Conference on Electronic Commerce,1999:158-166.
[3]邓爱林.电子商务推荐系统关键技术研究[博士毕业论文].上海:复旦大学,2003.
[4]邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1627.
[5]余力.电子商务个性化--理论、方法与应用[M].北京:清华大学出版社.2007.
[6]Jiawei Han,Micheline Kamber著.范明,孟小峰 译.数据挖掘概念与技术[M].北京:机械工业出版社,2007.410-419.
[7]Paul Resnick,Hal R.Varian.Recommender systems[J].Communications of ACM,1997.
[8]Kohavi,R.Applications of Data Mining to Electronic Commerce[J].Data Mining and Knowledge Discovery,2001.
[9]易明.基于Web挖掘的电子商务个性化推荐机理与方法研究[[博士学位论文].武汉:华中科技大学.2006.
[10]Ghani,R.and Fano,A.Building Recommender Systems using a Knowledge Base of Product Semantics.In Proceedings of Workshop on Recommendation and Personalization in eCommerce,2002.
[11]Bamshad Mobasher,Jin X,Zhou YZ.Semantically Enhanced Collaborative Filtering on the Web.In:Proceedings of the 1st European Web Mining Forum,Cavtat-Dubrovnik,Croatia,2003.
[12]Tim Berners-Lee,James Hendler,Ora Lassila.The Semantic Web[J].Scientific American,2001,284(5):34-43.
[13]宋炜,张铭.语义网简明教程[M].北京:高等教育出版社,2004.10-15.
[14]Tim Berners-Lee.XML and the Web[EB/OL].http://www.w3.org/2000/Talks/0906-xmlweb-tbl,2009-02-03.
[15]Grigoris Antoniou,Frank van Harmelen.A Semantic Web Primer[M].Massachusetts:The MIT Press Cambridge,2004.5-20.
[16]Tak,W.Y.,Matthew,J.,G.M.,and Umeshwar,D.From User Access Patterns to Dynamic Hypertext Linking[C].In Proceedings of 5~(th) International World Wide Web Conference,1996.
[17]Mobasher,B.,Dai,H.,Luo,T.,Nakagawa,N.,Sun,Y.,and Wiltshire,J.Discovery of Aggregate Usage Profiles for Web Personalization[C].In Proceedings of the Web Mining for E-Commerce Workshop,2000.
[18]Mobasher,B.,Cooley,R.,and Srivastava,J.Creating Adaptive Web Sits Through Usage-Based Clustering of URLs[C].In Proceedings of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop,1999.
[19]Xin Jin,Yanzan Zhou and B.Mobasher.A Maximum Entropy Web Recommendation System:Combining Collaborative and Content Features[C].In Proceedings of the 12~(th) ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Chicago,IL,USA.August 21-24,2005:612-617.
[20]X.Jin,Y Zhou,and B.Mobasher.A Unified Approach to Personalization Based of Probabilistic Latent Semantic Models of Web Usage and ContentfC].In Proceedings of the AAAI2004 Workshop on Semantic Web Personalization.San Jose,2004.
[21]Raymond J.Mooney and Loriene Roy.Content Based Book Recommending Using Learning for Text Categorization[C].In Proceedings of the 5 ACM Conference on Digital Libraries.San Antonio,2000:195-204.
[22]C.Basu,H.Hirsh and W.Cohen.Recommendation as classification:Using social and content-based information in recommendation[C].In Proceedings of the 5 National Conference on Artificial Intelligence,1998:714-720.
[23]Rayid Ghani and Andrew Fano.Building Recommender Systems using a Knowledge Base of Product Semantics[C].In Proceedings of the 2~(nd) International Conference on Adaptive Hypermedia and Adaptive Web Based Systems,Malaga,2002.
[24]B.Mobasher and H.K.Dai.A road map to more effective web personalization:Integrating domain knowledge with web usage mining[C].In Proceedings of International Conference on Internet Computing,Las Vegas,Nevada.2003.
[25]H.K.Dai and B.Mobasher.Using ontologies to discover domain-level web usage profiles[C].In 2~(nd) Workshop on Semantic Web Mining at the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases.Helsinki,Finland,2002:61-82.
[26]H.k.Dai and B.Mobasher.Integrating Semantic Knowledge with Web Usage Mining for Personalization.In Web Mining:Applications and Techniques[M].Anthony Scime (ed.),Idea Group Publishing,2005.
[27]Markob,Yoavs.Fab.Content-based collaborative recommendation [J].Communications of the ACM,1997,Vol.40(3):66-72.
[28]Pazzani Mj..A framework for collaborative,content-based and demographic filtering [J].Artificial Intelligence Review,1998,Vol.13(5):393-408.
[29]K.Lang.NewsWeeder:Learning to Filter News[C].In Proceedings of International Conference on Machine Learning,1995,pp.331-339.
[30]R.J.Mooney and L.Roy.Content-Based Book Recommending Using Learning for Text Categorization[C].In Proceedings of the Fifth ACM Conference on Digital Libraries,2000,pp.195-204.
[31]Sung-Shun Weng,Mei-Ju Liu.Feature-based recommendations for one-to-one marketing[J].Expert Systems with Applications,2004,Vo.26,pp.493-508.
[32]Prem M,Raymond J,Ramadass N.Content-boosted collaborative filtering for improved recommendations[R].Department of Computer Science,University of Texas Austin.
[33]Ravi Kumar,Prabhakar Raghavan,Sridhar Rajagopalan,and Andrew Tomkins.Recommendation Systems:A Probabilistic Analysis[J].Journal of Computer and System Sciences,2001,Vol.63,pp.42-61.
[34]周军锋,汤显,郭景峰.一种优化的协同过滤推荐算法.计算机研究与发展,2004.41(10):1843-1844.
[35]彭玉,程小平.基于属性相似性的Item-based协同过滤算法.计算机工程与应用,2007.43(14):144-145.
[36]Sarwar.Sparsity,scalability,and distribution in recommender systems[D].The University of Minnesota,2001.
[37]Paul Resnick,Hal R.Varian.Recommender systems[J].Communications of ACM,1997,40(3):56-58.
[38]Sarwar BM,Karypis G,Konstan JA,Riedl J.Application of dimensionality reduction in recommender system-A case study[C].In:Proc.of the ACM WebKDD 2000 Workshop,2000.
[39]Bleha,S.A.,Obaidat,M.S..Dimensionality reduction and feature extraction applications in identifying computer users[J].IEEE Transactions on Systems,Man and Cybernetics1991,Vol.21,No.2,pp.452 - 456.
[40]陆建江.加权关联规则挖掘算法的研究.计算机研究与发展,2002.10:1281-1286
[41]齐雁,李石君,薛海峰.对演变数据进行关联规则挖掘的新方法.计算机工程,2002.11:126-128.
[42]张峰,常会友,农杨.基于规则的电子商务推荐系统模型和实现.计算机集成制造系统,2004.(10):8:78-83.
[43]Eui-Hong(Sam) Han,George Karypis,Vipin Kumar,et al.Clustering based on association rule hypergraphs(position paper)[C].In Proceedings of the Workshop on Research Issues on Data Mining and Knowledge Discovery.Tucson,Arizona,1997:9-13.
[44]Ansari,S.,Kohavi,R.,Mason,L.,and Zheng,Z.J.Integrating E-Commerce and Data Mining:Architecture and Challenges[C].The IEEE International Conference on Data Mining,2001.
[45]Herlocker,J.Understanding and Improving Automated Collaborative Filtering Systems[D]. Ph.D.Thesis,Computer Science Dept,University of Minnesota,2000.
[46]Pazzani MJ.A framework for collaborative content-based and demographic filtering[J].Artificial Intelligence Review,13,6(5),393-408.
[47]Daniel M.Fleder,Kartik Hosanagar.Recommender systems and their impact on sales diversity[C].Proceedings of 8th ACM Conference on Electronic Commerce,San Diego,California,USA,2007.
[48]Sarwar BM,Karypis G,Konstan JA,et al.Analysis of recommendation algorithms for e-commerce[C].Proceedings of the ACM EC'00 Conference.Minneapolis,MN.2000
[49]Henng-Nam Kim,Ae-Ttie Ji,Geun-Sik Jo,Enhanced Prediction Algorithm for Item-Based Collaborative Filtering Recommendation[C].Proceedings of the 7th International Conference on Electronic Commerce and Web Technologies,Krakow,Poland,2006.
[50]刘平峰.基于知识网格的电子商务智能推荐理论方法研究[D].武汉:武汉理工大学管理学院,2006
[51]Bin Xiao,Esma A(i|¨)meur,Jos(?) Manuel Fernandez.PCFinder:An Intelligent Product Recommendation Agent for E-Commerce.IEEE International Conference on E-Commerce,June 24-27,2003.
[52]Mark Claypool,Phong Le,Makoto Wased,David Brown.Implicit Interest Indicators[C].In Proceedings of ACM Intelligent User Interfaces Conference(IUI),2001,pp.33-40.
[53]X.Fu,J.Budzik and K.J.Hammond.Mining navigation history for recommendation[C].In Proceedings of the 2000 International Conference on Intelligent User Interfaces.New Orleans,2000:106-112.
[54]Ohavi,R.Mining E-Commerce Data the Good,the Bad,and the Ugly[C].Invitedpaper/talk at KDD 2001 industrial track,2001.
[55]Oshadi Alahakoon,Seng Wai Loke,Arkady B.Zaslavsky.Capturing Buying Behaviour Using a Layered User Model[C].Proceedings of the 8th International Conference on Electronic Commerce and Web Technologies.Regensburg,Germany,2007.109-118.
[56]Dirk Bergemann,Deran Ozmen.Optimal pricing with Recommender Systems[C].Proceedings of 7th ACM Conference on Electronic Commerce,Ann Arbor,Michigan,USA,June 11-15,2006.
[57]Badrul M.Sarwar,George Karypis,Joseph Konstan,and John Riedl.Recommender Systems for Large-scale E-Commerce:Scalable Neighborhood Formation Using Clustering.Proceedings of the 5th International Conference on Computer and Information Technology,Dhaka,Bangladesh,2002.
[58]Charu.C.Aggarwal and P.S.Yu.Finding generalized projected clusters in high dimensional spaces[C].In Proceedings of Associatio
[59]B.M.Sarwar,G Karypis,J.A.Konstan,et al.Application of dimensionality reduction in recommender systems:a case study[C].In Proceedings of the WebKDD 2000 Workshop at the ACM SIGKKD 2000.Boston,2000:82-90.
[60]O.Etzioni:The World Wide Web:Quagmire or gold mine.Communications of the ACM,1996,39(11):65-68.
[61]R.Cooley,B.Mobasher,and J.Srivastava,Web mining:Information and Pattern discovery on the World Wide Web,In Proceedings of IEEE International Conference Tools with AI.1997.
[62]Raymond Kosala,Hendrik Blocked:Web Mining Research:A Survey,SIGKDD Explorations,2000,20(3):1-15.
[63]许建潮.Web挖掘中若干问题的研究[D].长春:吉林大学计算机学院,2005.
[64]Osmar Rachid Zaiane:Resource and Knowledge Discovery from the Internet and Multimedia Repositories[D],Canada:SIMON FRASER UNIVERSITY,2007.3.
[65]T.Bray,J.Paoli,C M.Sperberg-McQueen:Extensible Markup Language(XML) 1.0specification[EB/OL].World Wide Web Consortium Recommendation.http://www.w3.org/TR/REC-xml/.2007.
[66]Monika R.Henzinger and Krishna Bharat.Improved algorithms for topic distillation in a hyperlinked environment[C].In Proceedings of the 21~(st) International ACM SIGIR Conference on Research and Development in IR.2001:104-111.
[67]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.
[68]张志刚.领域本体构建方法的研究与应用[硕士学位论文].大连:大连海事大学,2008.
[69]何琳,杜慧平,侯汉清.领域本体的半自动构建方法研究[J].图书馆理论与实践,2007(5):26-28.
[70]肖敏.领域本体的构建方法研究[J].情报杂志,2006(2):70-72.
[71]Fensel D.The Semantic Web and its Languages[J].IEEE Computer Society,2000,15(6):67-73.
[72]W3C.OWL Web Ontology Language Overview[EB/OL].http://www.w3.org/TR/2004/REC-owl-features-20040210,2004-02-10.
[73]R.Fikes and D.McGuinness.An Axiomatic Semantics for RDF,RDF Schema and DAML+OIL,http://www.daml.org/2001/03/axiomatic-semantics.html,October 2001.
[74]周明建,高济.本体表示语言的研究及应用.计算机工程.2005.31(1):160-162.
[75]杜小勇,李曼,王大治.语义Web与本体研究综述.计算机工程.2004.24(10):14-20.
[76]Grigoris Antoniou and Frank van Harmelen,A Semantic Web Primer.The MIT Press Cambridge,Massachusetts,London,England,2004.4
[77]高曙.网格环境下面向服务的分布式可视化研究与实现[D].武汉:武汉理工大学计算机学院.2007.
[78]Robert Neches,Richard Fikes,Tim Finin,Thomas Gruber,Ramesh Patil,Ted Senator,and WIlliam Swartout.Enabling Technology for Knowledge Sharing[J].AI Magazine,1991,12(3):36-56.
[79]Thomas R.Gruber.A Translation Approach to Portable Ontology Specification[J].Knowledge Acquisition,1993,5(2):199-220.
[80]Pim Borst,Hans Akkermans,J Top.Engineering Ontologies[J].International Journal of Human-Computer Studies,1997,46(2-3):365-406.
[81]Steffen Staab,Rudi Studer,Hans-Peter Schnurr,et al.Knowledge Processes and Ontologies[J].IEEE Intelligent System,2001,16(1):26-34.
[82]Asunci(?)n G(?)mez P(?)rez.,V.Richard Benjamins overview of knowledge sharing and reuse components ontologies and problem-solving methods[C].Proceedings of the IJCAI-99workshop on Ontologies and Problem-Solving Methods(KRR5) Stockholm,Sweden,August 2,1999:1-15.
[83]Franz Badder,Diego Calvanses,Deborah McGuinness,Daniele Nardi and Peter Patel-Schneider.The Description Logic Handbook:Theory,Implementation and Applications.Cambridge University Press,2003
[84]宋炜,张铭.语义网简明教程[M].北京:高等教育出版社,2004.135-151.
[85]Franz Badder,Ian Horrocks and Ulrike Sattler.Description logics as ontology languages for the Semantic Web.Lecture Notes in Artificial Intelligence.Springer,2003.
[86]Ian Horrocks,Peter F Patel-Schneider and Frank van Harmelen.From SHIQ and RDF to OWL:The making of a web ontology language.Journal of Web Semantics,2003.
[87]Oscar Corcho,M.Fern(?)ndez,A.G(?)mez-P(?)rez,2003.Methodologies,tools,and languages for building ontologies.Where is their meeting point? Data&Knowledege Engineering,46:41-64.
[88]Matthew Horridge,Holger Knublauch,Alan Rector,Robert Setvens,Chris Wroe,.A Practical Guide to Building OWL Ontology Using the Prot(?)g(?)-OWL Plugin and COODE Tools.2004-8-27.The University of Manchester,2004.
[89]Horrocks,I.DAML+OIL:A Description Logic for the Semantic Web[J].IEEE Data Engineering Bulletin,2002,25(1):4-9.
[90]Matthew Horridge,Holger Knublauch,Alan Rector,Robert Setvens,Chris Wroe,A Practical Guide to Building OWL Ontology Using the Prot(?)g(?)-OWL Plugin and CO-ODE Tools[EB/OL].http://www.co-ode.org/resources/tutorials/ProtegeOWLTutorial.pdf,2004-8-27.
[91]Brian McBride.An Introduction to RDF and the Jena RDF API[R/OL].http://jena.sourceforge.net/tutorial/RDF_API/index.html,2007-3-21
[92]Jena2 Ontology API[R/OL].http://jena.sourceforge.net/ontology/index.html.2007-6-1.
[93]Dave Reynolds,Jena2 Inference supports[R/OL]. http://jena.sourceforge.net/inference/index.html,2007/03/23.
[94]Aduna.OpenRDF Sesame Core 3.0-alphal API[EB/OL].http://www.openrdf.org/doc/sesame3/3.0-alphal/apidocs,2009-01-23.
[95]Marta Millan,Maria Trujillo,Edward Ortiz.A Collaborative Recommender System Based on Asymmetric User Similarity.In:Proceedings of the 8th Intelligent Data Engineering and Automated Learning International Conference,Birmingham,UK 2007.
[96]张光卫,李德毅,李鹏,康建初,陈桂生.基于云模型的协同过滤推荐算法[J].软件学报,2007,18(10):2403-2411.
[97]Guandong Xu.Web Mining Technologies for Recommendation and Personalization[D].Australia:The School of Computer Science & Mathematics Faculty of Health,Engineering & Science,Victoria University,2008.
[98]Bharath Kumar Mohan,Benjamin J.Keller,Naren Ramakrishnan.Scounts,promoters,and connectors:the roles of ratings in nearest neighbor collaborative filtering[C].Proceedings of 7th ACM Conference on Electronic Commerce,Ann Arbor,Michigan,USA,June 11-15,2006.
[99]Robin B.Hybrid recommender systems:survey and experiments[R].Department of Information Systems and Decision Sciences,California State University,Fullerton.
[100]Trujillo,M.,Millan,M.,Ortiz,E.:A Recommender System Based on Multi-features.In:Proceedings of 2007 International Conference on Computational Science and Its Applications,ICCSA,Springer,Heidelberg.2007.
[101]Wang,J.,de Vries,A.,Reinders,M.:Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion.In:Proceedings of the 29th Annual International ACM SIGIR conference on Research and Development in Information Retrieval Swattle,Washington,USA.2006.
[102]Bamshad Mobasher,Xin Jin,Yanzan Zhou.Semantically Enhanced Collaborative Filtering on the Web.In:Proceedings of the 2nd European Web Mining Forum,Pisa,Italy,2004.
[103]张丙奇.基于领域知识的协同过滤算法[J].计算机工程,2005,31(21):7-9
[104]Wujian Yang,Zebing Wang,Mingyu You.An improved Collborative Filtering Method for Recommendations' Generation[C].In:Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,Hague,Netherlands 2004.
[105]C.H.Cheng,A.W.Fu,Y.Zhang.Entropy-based Subspace Clustering for Mining Numerical Data.In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-99).San Diego,Aug 1999:84-93.
[106]Craven,M.,Dipasquo,D.,Freitag,D.,McCallum,A.,etc.Learning to Construct Knowledge bases from the World Wide Web[J].Artificial Intelligence,2000,118(1-2):69-113.
[107]陶皖,李平,廖述梅.当前基于本体的语义标注工具的分析[J].安徽工程科技学院学报,2005,20(2):52-55.
[108]Parallel Understanding Systems Group.The SHOE Knowledge Annotator[EB/OL].http://www.cs.umd.edu/projects/plus/SHOE/KnowledgeAnnotator.html,2009-01.
[109]Bob Balzer;Neil Goldman;Marcelo Tallis.Semantic Markup Plug-In for MS Internet Explorer[EB/OL].http://annotation.semanticweb.org/Members/lago/semanticmarkupplug-informsi nternetexplorer.2009-01
[110]Marcelo Tallis.Semantic Word Processing for Content Authors[C].In Proceedings of 2~(nd)International Conference on Knowledge Caputre.Sanibel,Florida,USA.October 26,2003.
[111]W3C.Annotea Project[EB/OL].http://www.w3.org/2001/Annotea/.2009-01.
[112]Fabio Ciravegna,A lexiei Dingli.User-Sytem Cooperation in Document Annotation based on Information Extraction[C].In Proceedings of 13~(th) International Conference on Knowledge Engineering and Knowledge Management.Siguenza,Spain.October 1-4,2002.
[113]Damljanovic,D.;Tablan,V.;Bontcheva,K.A Text-based Query Interface to OWL Ontologies[C].In Proceedings of 6~(th) Language Resources and Evaluation Conference.2008.
[114]Marcelo Tallis,Neil M Goldman,Robert M Blazer.The Briefing Associate:Easing Authors to the Semantci Web[J].IEEE Intelligent Systems,2002,17(1):26-32.
[115]Siegfried Handschuh,Steffen Staab.Authoring and Annotation of Web Pages in CREAM[EB/OL].http://www.aifb.uni-karlsruhe.de/WBS/sha/papers/aa_cream_www11.pdf,2008-10
[116]Aditya Kalyanput,James Hendler,et al.http://www.mindswap.org/papers/SMORE.pdf,2008-10.
[117]史忠植,蒋运承,张海俊,董明楷.基于描述逻辑的主体服务匹配[J].计算机学报,2004,27(5).625-635.
[118]吴健,吴朝辉,李莹,邓水光.基于本体论和项目语义相似度的Web服务发现[J].计算机学报,2005,28(4).595-602.
[119]唐杰,梁邦勇,李涓子,王克宏.语义Web中的本体自动映射[J].计算机学报,2006,29(11).1956-1976.
[120]王家琴,李仁发,李仲生,唐剑波.一种基于本体的概念语义相似度方法的研究[J].计算机工程,2007,33(11).201-203.
[121]Risto Gligorov,Zharko Aleksovski,Warner ten Kate,Frank van Harmelen.Using Google Distance to Weight Approximate Ontology Matches[C].In:Proceedings of the International World Wide Web Conference(WWW).(2007),767-775.
[122]Danushka Bollegala,Yutaka Matsuo,Mitsuru Ishizuka.Measuring Semantic Similarity between Words Using Web Search Engines[C].In:Proceedings of the International World Wide Web Conference(WWW).(2007),757-766.
[123]Ganesan P,Garacia-molina H.Hierarchical Domain Structure to Compute Similarity[J].ACM Transaction on System,2003,21(1):64-93.
[124]Juell P.,Paulson P.Using reinforcement learning for similarity assessment in case-based systems[J].IEEE Intelligent Systems.2003,18(4):60-67.
[125]王进.基于本体的语义信息检索研究[博士学位论文].合肥:中国科技大学,2006.
[126]W3C.Web Characterization Terminology & Definitions Sheet[EB/OL].http://www.w3.org/1999/05/WCA-terms/,1999-05-24.
[127]张凯.基于本体的Web信息集成若干关键技术研究[D].上海:复旦大学信息与工程学院,2004.4-14.
[128]唐杰,梁邦勇,李涓子,王克宏.语义Web中的本体自动映射[J].计算机学报,2006,29(11).1956-1976.
[129]Xiao Min,Zhang Hongfei,Xiong Qianxing.A Collaborative filtering recommendation algorithm based on Domain Knowledge[C].ISCID2008,Wuhan China,October17-18,2008.
[130]郑运刚,马建国.基于分类的用户兴趣漂移模型.情报杂志,2008.1:37-38.
[131]肖敏,熊前兴.基于项目语义相似度的协同过滤推荐算法[J].武汉理工大学学报·自科版.2009.3.
[132]刘乃答,江志恒.关于艾滨浩斯保持曲线的注记[M].国家测绘局测绘科学研究所出版社,58-61.
[133]江志恒,刘乃答.论遗忘函数-关于记忆心理学的数学讨论[M].国家测绘局测绘科学研究所出版社,58-60.
[134]郑先荣,曹先彬.线性逐步遗忘协同过滤算法的研究[J].计算机工程,2007.33(6):72-73。
[135]邢春晓,高凤荣,战思南,周立柱.适应用户兴趣变化的协同过滤推荐算法[J].计算机研究与发展,2007.44(2):296-301.
[136]郑先荣,汤泽滢,曹先彬.适应用户兴趣变化的非线性逐步遗忘协同过滤算法[J].计算机辅助工程,2007.16(2):69-71.
[137]Byeong Man Kim,Qing Li.Probabilistic Model Estimation for Collaborative Filtering Based on Items Attributes[C].In:Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence,2004.
[138]Mobasher,B.,Cooler,R.,& Srivastava,J.Automatic Personalization based on Web Usage Mining[J].Communications of the ACM.2000,43(8):142-151.
[139]Robin B.Hybrid recommender system.Survey and experiments[J].User Modeling and User Adapted Interaction.2002.12(4):331-370.
[140]Ai Bo Song,Mao Xian Zhao,Zuo Peng Liang.Discovering User Profiles for Web Personalized Recommendaton[J].Computer.Science and Technology.2004.19(3):320-328.
[141]B.Mobasher.Web Usage Mining and Personalization.In Practical Handbook of Internet Computing[M].Munindar P.Singh(ed.),CRC Press,2005.
[142]李超锋.Web使用挖掘关键技术研究[博士学位论文].武汉:华中科技大学.2007.
[143]Alexander Hinneburg,Daniel A.Keim.A General Approach to Clustering in Large Databases with Noise[J].Knowledge and Information Systems.2003,44(5):387-415.
[144]王美华.数据挖掘领域中的聚类方法[J].南华大学学报(理工版),2004,18(1):58-62.
[145]Robert Cooley,Bamshad Mobasher,Jaideep Srivastava:Data Preparation for Mining World Wide Web Browsing Patterns.Knowl[J].Inf.Syst.1(1):5-32.
[146]姚洪波,杨炳儒.Web日志挖掘数据预处理过程技术研究[J].微计算机信息,2006,22(6-3):234-236.