基于计算语用学和项目的资源协同过滤推荐研究
|
摘要
随着Internet的发展,网上的各类资源都以惊人的速度增长着。要及时地在网络的海量信息中发现所需要的资源已经变得越来越困难,用户极需一种推荐系统帮助他们。个性化推荐系统可以帮助用户找到所需信息,能有效留住用户、提高网站的点击率和用户的忠诚度。进而辅助企业达到个性化营销的目的,提升销售量,创造最大的利润。个性化服务概念的兴起,也使得越来越多领域开始重视推荐系统的应用。个性化推荐系统在这种良好的发展趋势的推动和应用前景下,逐渐成为Web智能技术的一个重要研究内容,得到了众多研究者的广泛关注。
近年来,协同过滤推荐技术在理论和实践中都得到了快速的发展,但是随着其应用系统规模的进一步扩大,它面临着一系列新的挑战。与此同时,把语用学理论应用于信息系统也逐步成为一个研究热点。语用学是研究符号与解释者之间的关系,研究一定语境条件下的语言和符号产生含义的理解及其产生的效果。本论文将计算语用学的基本思想应用于推荐系统中,包括对推荐系统中个性化情境分析、用户信任等级计算模型、基于情境及信任的推荐方法和算法的应用研究。
研究开创性地提出了个性化情境和用户信任等级的概念,从一个新颖的角度解决了协同过滤推荐中的情境缺失和独立性假设问题,提高了推荐系统的推荐质量与抗评分攻击能力;同时,计算语用学在个性化推荐领域的应用研究对语用学本身的发展也具有推动作用。现将论文的主要研究内容和成果概括如下:
①对个性化推荐系统目前的总体发展情况进行了综述。探讨了个性化概念的界定,总结并归类了现有的推荐技术,指出其各自的特点、适用范围;在此基础上,对协同过滤算法的目前研究进展进行总结、分类,并指出存在的问题,引出本文的研究意义,为下一步研究奠定理论基础。
②对语用学的发展及其与个性化推荐的关系进行了分析。简述了语用学的发展历史,分析了语用学研究与个性化推荐研究的相似性,提出了计算语用学三要素,即溯因推理、信任和情境与个性化推荐的关系,为基于语用学的个性化资源推荐的研究奠定了理论基础。
③提出个性化情境的概念、最显著个性化情境因素和多因素个性化情境等概念。把个性化情境引入协同过滤推荐系统,构建了基于个性化情境的推荐方法来解决情境缺失问题。然后提出基于最显著个性化情境因素的协同过滤推荐算法,通过实验证明最显著个性化情境因素对评分预测准确性的提高是有帮助的。进而提出基于多因素个性化情境的推荐方法,采用BP神经网络和RBF神经网络进行情境因素权重的学习,得到基于神经网络的个性化评分预测模型后进行评分预测和推荐,并通过实验验证了算法的有效性,说明多因素个性化情境的推荐更能提高评分预测的准确性。实验还证明径向基神经网络更能提高预测的准确性,基于多因素个性化情境的推荐算法比传统算法有更好的调和平均值和稳定性。
④把信任引入协同过滤推荐系统,构建了一种基于信任的协同过滤推荐方法,为解决用户独立性假设问题提供了一种新的思路和方法。在分析信任的定义、性质以及信任与推荐的关系的基础上,提出基于用户兴趣相似性、评分相似性和评分相关性来构建用户关联图的方法,提出基于PageRank用户信任等级的UserRank计算方法,进而提出了基于用户信任等级的协同过滤推荐算法,并通过实验验证了算法的有效性和优越性。实验结果表明:将用户信任等级与经典的推荐算法结合,在不影响预测准确性的前提下可以提高算法防范评分攻击的能力。最后,提出基于情境和信任的综合推荐方法,作为一个综合情境因素和信任因素进行推荐的试探性工作。
⑤将以上研究提出的个性化情境分析方法、信任等级计算方法和几种推荐方法用于构建一个基于语用的学习资源个性化推荐系统,列出了系统的体系结构、功能模块设计和结果展示等内容。
The volume of information over the Internet is increasing at a tremendous rate. Users are usually confronted with situations in which they have been exposed with too many options to choose from. They need help to explore and to filter out irrelevant information based on their preferences. The corresponding requirement is to describe and capture relevant information for directing users based on specific interests and needs. The supporting systems are identified as personalized recommendation systems. The recommendation systems will also act as a stimulant to further E-Commerce sales growth by converting browsers into buyers, preventing user losing, increasing clicks-ratio, and building customer loyalty. Recently recommendation systems have gradually become an important part in Web Intelligence technologies. Personalized recommendation approaches have gained great momentum both in the commercial and research areas.
Recent years, collaborative filtering recommendation technique has gain rapid growth in theory and practice, but along with the scale of the application systems further expanding, it is facing a series of new challenges. At same time, pragmatics theory applied to information systems has also gradually become a research hotspot. This dissertation is focused on the application of the basic ideas of computational pragmatics in recommendation systems. The main research includes: the personalized context analysis, the model for user belief rank computing, recommendation methodologies and algorithms based on context and belief. The research is to solve the context-free, independence-assumption, and shilling-attacks problem in collaborative filtering recommendation approaches from a novel perspective, and to improve the quality of the recommendations. Meanwhile the application of computational pragmatics to personalized recommendation domain will also improve the development of the pragmatics. The main research and contributions in this dissertation are summarized as follows:
①The current state-of-the-art methods and techniques of personalized recommendation systems are reviewed. In the dissertation, recommendation techniques are summarized, their characteristics and the scope of application are analyzed. On that basis, the research on collaborative filtering algorithms is summarized and classified, and the problem of the research was analyzed.
②The development of pragmatics and the relationships and similarities between pragmatics and personalized recommendation are analyzed. The relationships between the three key concepts of computational pragmatics and personalized recommendation are further analyzed. The research has set up the theoretical foundation for the research on pragmatics-based personalized recommendation.
③Several approaches are proposed to solve the context-free problem by incorporating personalized contextual information into collaborative filtering recommendation systems. Firstly, a collaborative filtering recommendation algorithm based on the most significant personalized contextual parameter is proposed. The experimental results show that our approach is helpful to improve the precision of rating prediction. Secondly, a collaborative filtering recommendation approach based on personalized multi-contextual parameter is proposed. In this approach, a rating prediction model is learned by BP and RBF neural networks. Then the model is used to extrapolate unknown ratings and make recommendations. An experiment is given to evaluate the approach. The experimental results show that the approach is more helpful to improve the precision of rating prediction. The RBF neural network is more helpful than BP is.
④A user belief rank based collaborative filtering recommendation approach is proposed. The approach provides a novel idea and method for the independence-assumption problem. Firstly, the definition and characteristics of belief, and the relationships between belief and recommendation is analyzed. Secondly, on that basis, a PageRank-based UserRank (belief rank) algorithm is proposed. Thirdly, and two collaborative filtering recommendation algorithms are proposed based on UserRank and Adjusted Cosine, and UserRank and Slope One. Finally, the experiments show that our approaches provide better recommendation results than Adjusted Cosine and Slope One. At the end, as trial research for the recommendation effectiveness based on the combination of contextual information and belief information, a comprehensive recommendation algorithm is proposed.
⑤A pragmatics-based learning resource personalized recommendation system is proposed. In the system, the analysis approach for personalized context, the computation method for user rank, and the personalized context based and user rank based recommendation approaches are applied. The architecture, functional modules, and system interfaces are provided.
近年来,协同过滤推荐技术在理论和实践中都得到了快速的发展,但是随着其应用系统规模的进一步扩大,它面临着一系列新的挑战。与此同时,把语用学理论应用于信息系统也逐步成为一个研究热点。语用学是研究符号与解释者之间的关系,研究一定语境条件下的语言和符号产生含义的理解及其产生的效果。本论文将计算语用学的基本思想应用于推荐系统中,包括对推荐系统中个性化情境分析、用户信任等级计算模型、基于情境及信任的推荐方法和算法的应用研究。
研究开创性地提出了个性化情境和用户信任等级的概念,从一个新颖的角度解决了协同过滤推荐中的情境缺失和独立性假设问题,提高了推荐系统的推荐质量与抗评分攻击能力;同时,计算语用学在个性化推荐领域的应用研究对语用学本身的发展也具有推动作用。现将论文的主要研究内容和成果概括如下:
①对个性化推荐系统目前的总体发展情况进行了综述。探讨了个性化概念的界定,总结并归类了现有的推荐技术,指出其各自的特点、适用范围;在此基础上,对协同过滤算法的目前研究进展进行总结、分类,并指出存在的问题,引出本文的研究意义,为下一步研究奠定理论基础。
②对语用学的发展及其与个性化推荐的关系进行了分析。简述了语用学的发展历史,分析了语用学研究与个性化推荐研究的相似性,提出了计算语用学三要素,即溯因推理、信任和情境与个性化推荐的关系,为基于语用学的个性化资源推荐的研究奠定了理论基础。
③提出个性化情境的概念、最显著个性化情境因素和多因素个性化情境等概念。把个性化情境引入协同过滤推荐系统,构建了基于个性化情境的推荐方法来解决情境缺失问题。然后提出基于最显著个性化情境因素的协同过滤推荐算法,通过实验证明最显著个性化情境因素对评分预测准确性的提高是有帮助的。进而提出基于多因素个性化情境的推荐方法,采用BP神经网络和RBF神经网络进行情境因素权重的学习,得到基于神经网络的个性化评分预测模型后进行评分预测和推荐,并通过实验验证了算法的有效性,说明多因素个性化情境的推荐更能提高评分预测的准确性。实验还证明径向基神经网络更能提高预测的准确性,基于多因素个性化情境的推荐算法比传统算法有更好的调和平均值和稳定性。
④把信任引入协同过滤推荐系统,构建了一种基于信任的协同过滤推荐方法,为解决用户独立性假设问题提供了一种新的思路和方法。在分析信任的定义、性质以及信任与推荐的关系的基础上,提出基于用户兴趣相似性、评分相似性和评分相关性来构建用户关联图的方法,提出基于PageRank用户信任等级的UserRank计算方法,进而提出了基于用户信任等级的协同过滤推荐算法,并通过实验验证了算法的有效性和优越性。实验结果表明:将用户信任等级与经典的推荐算法结合,在不影响预测准确性的前提下可以提高算法防范评分攻击的能力。最后,提出基于情境和信任的综合推荐方法,作为一个综合情境因素和信任因素进行推荐的试探性工作。
⑤将以上研究提出的个性化情境分析方法、信任等级计算方法和几种推荐方法用于构建一个基于语用的学习资源个性化推荐系统,列出了系统的体系结构、功能模块设计和结果展示等内容。
The volume of information over the Internet is increasing at a tremendous rate. Users are usually confronted with situations in which they have been exposed with too many options to choose from. They need help to explore and to filter out irrelevant information based on their preferences. The corresponding requirement is to describe and capture relevant information for directing users based on specific interests and needs. The supporting systems are identified as personalized recommendation systems. The recommendation systems will also act as a stimulant to further E-Commerce sales growth by converting browsers into buyers, preventing user losing, increasing clicks-ratio, and building customer loyalty. Recently recommendation systems have gradually become an important part in Web Intelligence technologies. Personalized recommendation approaches have gained great momentum both in the commercial and research areas.
Recent years, collaborative filtering recommendation technique has gain rapid growth in theory and practice, but along with the scale of the application systems further expanding, it is facing a series of new challenges. At same time, pragmatics theory applied to information systems has also gradually become a research hotspot. This dissertation is focused on the application of the basic ideas of computational pragmatics in recommendation systems. The main research includes: the personalized context analysis, the model for user belief rank computing, recommendation methodologies and algorithms based on context and belief. The research is to solve the context-free, independence-assumption, and shilling-attacks problem in collaborative filtering recommendation approaches from a novel perspective, and to improve the quality of the recommendations. Meanwhile the application of computational pragmatics to personalized recommendation domain will also improve the development of the pragmatics. The main research and contributions in this dissertation are summarized as follows:
①The current state-of-the-art methods and techniques of personalized recommendation systems are reviewed. In the dissertation, recommendation techniques are summarized, their characteristics and the scope of application are analyzed. On that basis, the research on collaborative filtering algorithms is summarized and classified, and the problem of the research was analyzed.
②The development of pragmatics and the relationships and similarities between pragmatics and personalized recommendation are analyzed. The relationships between the three key concepts of computational pragmatics and personalized recommendation are further analyzed. The research has set up the theoretical foundation for the research on pragmatics-based personalized recommendation.
③Several approaches are proposed to solve the context-free problem by incorporating personalized contextual information into collaborative filtering recommendation systems. Firstly, a collaborative filtering recommendation algorithm based on the most significant personalized contextual parameter is proposed. The experimental results show that our approach is helpful to improve the precision of rating prediction. Secondly, a collaborative filtering recommendation approach based on personalized multi-contextual parameter is proposed. In this approach, a rating prediction model is learned by BP and RBF neural networks. Then the model is used to extrapolate unknown ratings and make recommendations. An experiment is given to evaluate the approach. The experimental results show that the approach is more helpful to improve the precision of rating prediction. The RBF neural network is more helpful than BP is.
④A user belief rank based collaborative filtering recommendation approach is proposed. The approach provides a novel idea and method for the independence-assumption problem. Firstly, the definition and characteristics of belief, and the relationships between belief and recommendation is analyzed. Secondly, on that basis, a PageRank-based UserRank (belief rank) algorithm is proposed. Thirdly, and two collaborative filtering recommendation algorithms are proposed based on UserRank and Adjusted Cosine, and UserRank and Slope One. Finally, the experiments show that our approaches provide better recommendation results than Adjusted Cosine and Slope One. At the end, as trial research for the recommendation effectiveness based on the combination of contextual information and belief information, a comprehensive recommendation algorithm is proposed.
⑤A pragmatics-based learning resource personalized recommendation system is proposed. In the system, the analysis approach for personalized context, the computation method for user rank, and the personalized context based and user rank based recommendation approaches are applied. The architecture, functional modules, and system interfaces are provided.
引文
[1] Gao, M., K. Liu, and Z. Wu, Personalisation in web computing and informatics: theories, techniques, applications, and future research [J]. Information Systems Frontiers. DOI:10.1007/s10796-009-9199-3, 2009.
[2]徐少春.管理软件个性化的轮回. ERP改变中国2007 [Z]; Available from: http://kingdee.e-works.net.cn/document/200708/article1926.htm.
[3]曹渝昆,基于神经网络和模糊逻辑的智能推荐系统研究[D],计算机学院. 2006,重庆大学:重庆.
[4]郭艳红,推荐系统的协同过滤算法与应用研究[D]. 2008,大连理工大学:大连.
[5] Shardanand, U. and P. Maes. Social information filtering: algorithms for automating“word of mouth”[C]. SIGCHI. 1995: ACM Press/Addison-Wesley Publishing Co.
[6] Goldberg, D., et al., Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992. 35(12): 70.
[7]黄永文,中文产品评论挖掘关键技术研究[D],计算机学院. 2009,重庆大学:重庆.
[8] Resnick, P. and H.R. Varian, Recommender systems [J]. Communications of the ACM, 1997. 40(3): 57.
[9] Al Mamunur Rashid, I.A., et al. Getting to know you: Learning new user preferences in recommender systems [M]. 2002: Citeseer.
[10] Linden, G., B. Smith, and J. York, Amazon.com recommendations: item-to-item collaborative filtering [J]. IEEE Internet computing, 2003. 7(1): 76-80.
[11] Das, A.S., et al., Google news personalization: scalable online collaborative filtering [C]. Proceedings of the 16th international conference on World Wide Web, 2007: 271-280.
[12] Di Giacomo, M., et al., MyLibrary, a personalization service for digital library environments [C]. Proceedings of the Second DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries (ERCIM Workshop Proceedings 01/W03). Dublin, 2001.
[13] Renda, M.E. and U. Straccia, A personalized collaborative Digital Library environment: a model and an application [J]. Information Processing and Management, 2005. 41(1): 5-21.
[14] Mooney, R.J. and L. Roy. Content-based book recommending using learning for text categorization [C]. Proceedings of the fifth ACM conference on Digital libraries. 2000. ACM New York, NY, USA.
[15] Li, Y., L. Lu, and L. Xuefeng, A hybrid collaborative filtering method for multiple-interestsand multiple-content recommendation in E-Commerce [J]. Expert Systems with Applications, 2005. 28(1): 67-77.
[16] Conlan, O., et al., Multi-model, metadata driven approach to adaptive hypermedia services for personalized elearning [C]. Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 2002: 100-111.
[17] Chen, C.M. and L.J. Duh, Personalized web-based tutoring system based on fuzzy item response theory [J]. Expert Systems with Applications, 2008. 34(4): 2298-2315.
[18] Fu, Y., A semiotic approach for modelling semantics in content management [D], in School of Systems Engineering. 2007, University of Reading.
[19] Mock, K.J. and V.R. Vemuri, Information filtering via hill climbing, WordNet, and index patterns [J]. Information Processing and Management, 1997. 33(5): 633-644.
[20] Sakagami, H. and T. Kamba, Learning personal preferences on online newspaper articles from user behaviors [J]. Computer Networks and ISDN Systems, 1997. 29(8-13): 1447-1455.
[21] Lai, H.J., T.P. Liang, and Y.C. Ku, Customized Internet news services based on customer profiles [C]. Proceedings of the 5th International Conference on Electronic Commerce, 2003: 225-229.
[22] Liang, T.-P., et al., A semantic-expansion approach to personalized knowledge recommendation [J]. Decision Support Systems, 2007. In Press, Corrected Proof.
[23] Chedrawy, Z. and S.S.R. Abidi. Case based reasoning for information personalization: using a context-sensitive compositional case adaptation approach [C]. Engineering of Intelligent Systems, 2006 IEEE International Conference on. 2006.
[24] Mobasher, B., Data mining for web personalization [M]. The Adaptive Web: Methods and Strategies of Web Personalization, Brusilovsky, P., Berlin Heidelberg New York: Springer Verlag, 2007.
[25] Morris, C.W., Foundations of the theory of signs [M]. 1938: The University of Chicago Press.
[26] Davidson, D. and G. Harman, Semantics of Natural Languages [J]. Philosophy of Language: The Central Topics, 2007: 57.
[27] Akmajian, A., et al., Linguistics: An introduction to language and communication [M]. 2001: The MIT press.
[28] Liu, K., Semiotics in information systems engineering [M]. 2000: Cambridge University Press.
[29] Jurafsky, D., Pragmatics and computational linguistics [M]. Handbook of Pragmatics.Blackwell, Oxford, 2002.
[30] Bunt, H. and B. Black, The ABC of computational pragmatics. Abduction Belief and Context in Dialogue [J]. Studies in Computational Pragmatics, 2000:1-46.
[31] Bunt, H. and W. Black, Abduction, belief and context in dialogue: studies in computational pragmatics [J]. Computational Linguistics, 2000. 28(1):96-98.
[32] Singh, M.P., The pragmatic web [J]. IEEE Internet computing, 2002:4-5.
[33] Singh, M.P., The pragmatic web: preliminary thoughts [C]. NSF-OntoWeb Workshop on Database and Information Systems Research for Semantic Web and Enterprises, Amicalola Falls State Park, GA, 2002.
[34]钟义信,面向智能研究的全信息理论:纪念Shannon信息论50周年[J].北京邮电大学学报, 1998. 21(004):1-6.
[35]陈彦萍,李增智,面向语用Web服务的网络服务管理机制研究[J].微电子学与计算机, 2008. 25(009): p. 31-33.
[36]翟社平,魏娟丽,李增智,基于参与者上下文概念意图的语用Web服务发现[J].计算机应用, 2009. 29(001):224-226.
[37]杨斌,易明,王学东,基于语用信息的站点个性化信息服务研究[J].情报理论与实践, 2008. 31(003): 457-460.
[38] Gori, M., et al., Itemrank: A random-walk based scoring algorithm for recommender engines [C], in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI). 2007: Hyderabad, India. p. 778-781.
[39] Montaner, M., B. Lopez, and J.L. De La Rosa, A taxonomy of recommender agents on the internet [J]. Artificial Intelligence Review, 2003. 19(4):285-330.
[40] Perugini, S. and N. Ramakrishnan, Personalizing web sites with mixed-initiative interaction [J]. IT Professional, 2003. 5(2):9-15.
[41] Riecken, D., Introduction: personalized views of personalization [J]. Communications of the ACM, 2000. 43(8):26-28.
[42] Frias-Martinez, E., et al., Automated user modeling for personalized digital libraries [J]. International Journal of Information Management, 2006. 26(3):234-248.
[43] Chiu, W. Web site personalization. 2001 [Z]; Available from: http://www.ibm.com/developerworks/websphere/library/techarticles/hipods/personalize.html.
[44] Weller, M., Virtual learning environments: using, choosing and developing your VLE. 2007, Oxon: Routledge.
[45] Zhang, Z., Community - the dawn of personalisation technology in the next generation Internet [Z]. http://googlechinablog.com/2007/08/blog-post_20.html 2007. 2008(June).
[46] Eirinaki, M. and M. Vazirgiannis, Web mining for web personalization [J]. ACM Transactions on Internet Technology, 2003. 3(1): 1-27.
[47] Murthi, B.P.S. and S. Sarkar, The role of the management sciences in research on personalization [J]. Management Science, 2003. 49(10):1344-1362.
[48] Adomavicius, G. and A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions [J]. Knowledge and Data Engineering, IEEE Transactions on, 2005. 17(6):734-749.
[49] Jeevan, V.K.J. and P. Padhi, A selective review of research in content personalization [J]. Library Review, 2006. 55(9):556-586.
[50] Montgomery, A. and M.D. Smith, Prospects for personalization on the Internet [M]. 2008: SSRN.
[51] Kim, J.K., et al., A personalized recommendation procedure for Internet shopping support [J]. Electronic Commerce Research and Applications, 2002. 1(3-4):301-313.
[52] Pazzani, M.J. and D. Billsus, Content-based recommendation systems. The Adaptive Web: Methods and Strategies of Web Personalization [J], Lecture Notes in Computer Science, 2006. 4321.
[53] Joachims, T., C. Nedellec, and C. Rouveirol. Text categorization with support vector machines: learning with many relevant [M]. 1998: Springer.
[54] Wei, C.P., C.S. Yang, and H.W. Hsiao, A collaborative filtering-based approach to personalized document clustering [J]. Decision Support Systems, 2008. 45(3):413-428.
[55] Buckley, C., et al. Automatic query expansion using SMART [C]. Overview of the Third Text Retrieval Conference (TREC 3). 1995: Diane Pub Co.
[56] Hofmann, T., Latent semantic models for collaborative filtering [J]. ACM Transactions on Information Systems (TOIS), 2004. 22(1):89-115.
[57] Hofmann, T., Probabilistic latent semantic analysis [J]. Matrix, 1999. 50: 2.
[58] Jin, X., Y. Zhou, and B. Mobasher, Web usage mining based on probabilistic latent semantic analysis [C]. Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004:197-205.
[59] Ansari, A., S. Essegaier, and R. Kohli, Internet recommendation systems [J]. Journal of Marketing Research, 2000. 37(3): 363-375.
[60] Min, S.H. and I. Han, Detection of the customer time-variant pattern for improving recommender systems [J]. Expert Systems with Applications, 2005. 28(2):189-199.
[61] Park, Y.-J. and K.-N. Chang, Individual and group behavior-based customer profile model for personalized product recommendation [J]. Expert Systems with Applications, 2008. InPress, Corrected Proof.
[62] Sarwar, B., et al., Item-based collaborative filtering recommendation algorithms [C], Proceedings of the 10th International Conference on World Wide Web. 2001: Hongkong. p. 285-295.
[63] Lemire, D. and A. Maclachlan. Slope One predictors for online rating-based collaborative filtering [M]. 2005: Society for Industrial Mathematics.
[64] Masters, J., T. Madhyastha, and A. Shakouri, ExplaNet: a collaborative learning tool and hybrid recommender system for student-authored explanations [J]. Journal of Interactive Learning Research, 2008. 19(1): 51-74.
[65]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(009):1621-1628.
[66]张锋,常会友,使用BP神经网络缓解协同过滤推荐算法的稀疏性问题[J].计算机研究与发展, 2006. 43(004): 667-672.
[67]孙小华,协同过滤系统的稀疏性与冷启动问题研究[D],计算机学院. 2005,浙江大学:杭州.
[68]李金秀,我国数字图书馆个性化信息服务探讨[J].图书馆工作与研究, 2005(003): p. 72-74.
[69] Adomavicius, G. and A. Tuzhilin, Multidimensional recommender systems: a data warehousing approach [J]. Lecture Notes in Computer Science, 2001. 2232.
[70] Morris, C.W., Signs, language and behavior. 1946: Prentice-Hall Englewood, NJ.
[71]张韧弦,形式语用学导论[M].复旦博学.语言学系列. 2008,复旦大学出版社:上海.
[72] Canhoto, A.I., Ontology-based interpretation and validation of mined knowledge: Normative and cognitive factors in data mining [C]. Data mining with ontologies: implementations, findings, and frameworks. 2007: Hershey, NY, Information Science Reference. 84-105.
[73] Liu, K. Incorporating Human Aspects into Grid Computing for Collaborative Work [C]. Keynote at ACM International Workshop on Grid Computing and e-Science. 2003. San Francisco.
[74] Liu, K. and A. Benfell, Pragmatic web– incorporating semiotics into web services [C], Keynote paper, 4th International Conference on Software and Data Technology, 26-29 July, 2009, Sofia, Bulgaria.
[75] Liu, K. Pragmatic computing: a semiotic perspective to web services [C], Keynote paper, Proceedings of ICETE2007, E-Business and Telecommunications, pp3– 15, Springer, ISSN 1865-0929.
[76] Montague, R., Pragmatics and intensional logic [J]. Dialectica, 1970. 24(4): p. 277-302.
[77]郭鸿,语用学的符号学分析[J].外语研究, 2005(005):1-4.
[78]文旭,追溯语用学的思想——语用学思想史探索之一[J].外语教学, 2003. 24(003): p. 5-10.
[79]刘根,李德华,计算语用学研究的途径和方法[J].计算机工程与应用, 2005. 41(025): 4-8.
[80]蒋严,论语用推理的逻辑属性——形式语用学初探[J].外国语:上海外国语大学学报, 2002(003):18-29.
[81] Gabbay, D.M. and J. Woods, The reach of abduction: insight and trial [M]. 2005: Elsevier Science.
[82]张大松,论科学思维的溯因推理[J].华中师范大学学报(哲社版), 1993(3):24-29.
[83] Rotter, J.B., A new scale for the measurement of interpersonal trust 1 [J]. Journal of personality, 1967. 35(4): 651-665.
[84]盛晓明,话语规则与知识基础[M]. 2000,上海:学林出版社.
[85]姚忠,吴跃,常娜,集成项目类别与语境信息的协同过滤推荐算法[J].计算机集成制造系统, 2008. 14(007):1449-1456.
[86] Chen, X., College English teaching in dynamic contexts [J]. Sino-US English Teaching, 2005. 2(11): p. 57-59.
[87]许海玲,互联网推荐系统比较研究[J].软件学报, 2009. 20(2): 350-362.
[88]李聪,梁昌勇,马丽,基于领域最近邻的协同过滤推荐算法[J].计算机研究与发展, 2008. 45(009):1532-1538.
[89]张光卫,面向场景的协同过滤推荐算法[J].系统仿真学报, 2006. 18(z2): 595-601.
[90] Canny, J. Collaborative filtering with privacy [Z]. 2002.
[91] Domingos, P. and M. Richardson, Mining the network value of customers [M]. 2001, ACM New York, NY, USA. p. 57-66.
[92]王宏宇,商务推荐系统的设计研究[D]. 2007,中国科技大学.
[93] Herlocker, J.L., et al., Evaluating collaborative filtering recommender systems [J]. ACM Transactions on Information Systems, 2004. 22(1): 5-53.
[94] Billsus, D. and M.J. Pazzani, A hybrid user model for news story classification [C]. Proceedings of the Seventh International Conference on User Modeling Table of Contents, 1999: 99-108.
[95] Sarwar, B., et al., Incremental singular value decomposition algorithms for highly scalable recommender systems [C], Fifth International Conference on Computer and Information Technology (ICCIT 2002). 2002: Shanghai, China. p. 399-404.
[96]邓爱林,左子叶,朱扬勇,基于项目聚类的协同过滤推荐算法[J].小型微型计算机系统,2004. 25(009):1665-1670.
[97]赵亮,胡乃静,个性化推荐算法设计[J].计算机研究与发展, 2002. 39(008):986-991.
[98] Cui, G., K. Liu, and Z. Yunchuan. Enhancing the agility in business processes through context-based modeling [C]. Service Operations and Logistics, and Informatics, 2008. IEEE/SOLI 2008. IEEE International Conference on. 2008. 156-164.
[99] Gao, M. and W. Zhongfu. Incorporating pragmatic information in personalized recommendation systems [C]. The 11th International Conference on Informatics and Semiotics in Organisations. 2009. p. 156-164.
[100] Dey, A.K., Understanding and using context [J]. Personal and Ubiquitous Computing, 2001. 5(1): p. 4-7.
[101] Dey, A.K. and G.D. Abowd. Towards a better understanding of context and context-Awareness [C]. CHI 2000 Workshop on the What, Who, Where, When, and How of Context-Awareness, 2000.
[102] Adomavicius, G., et al., Incorporating contextual information in recommender systems using a multidimensional approach [J]. ACM Transactions on Information Systems (TOIS), 2005. 23(1):103-145.
[103]冯在文,一种基于情境推理的语义Web服务发现方法[J].计算机学报, 2008. 31(008): 1354-1363.
[104]姜丽芬,卢桂章,辛运帏,基于RFID普适计算环境的形式化上下文模型[J].计算机工程, 2007. 33(011):173-175.
[105] Minns, A.W. and M.J. Hall, Artificial neural networks as rainfall-runoff models [J]. Hydrological Sciences Journal/Journal des Sciences Hydrologiques, 1996. 41(3): p. 399-417.
[106] Ilunga, M. and D. Stephenson, Infilling streamflow data using feed-forward back-propagation (BP) artificial neural networks: Application of standard BP and pseudo Mac Laurin power series BP techniques [M]. Water SA, 2005. 31(2):171-176.
[107]苏凌,基于径向基函数神经网络的港口吞吐量预测研究[D]. 2006,上海海事大学:上海.
[108]尹建川,径向基函数神经网络及其在船舶运动控制中的应用研究[D]. 2007,大连海事大学:大连.
[109]苏美娟,径向基函数神经网络学习算法研究[D]. 2007,苏州大学.
[110] Lemire, D. and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering [C]. Proceedings of the SIAM Data Mining Conference. 2005. Newport Beach, California: Society for Industrial Mathematics.
[111]葛哲学,孙志强,神经网络理论与MATLAB R2007实现[M]. 2007,北京:电子工业出版社.
[112]倪海燕,唐德根,语用预设的语篇功能体现[J].求索, 2005(011): p. 186-187.
[113]向长江,陈平,信任问题研究文献综述[J].广州大学学报:社会科学版, 2003. 2(005): 39-42.
[114]朱庆华,李亮,社会网络分析法及其在情报学中的应用[J].情报理论与实践, 2008. 31(002):179-183.
[115]胡勇,王陆,网络协作学习中的社会网络分析个案研究[J].开放教育研究, 2006.12(005): 56-61.
[116] Mobasher, B., et al., Analysis and detection of segment-focused attacks against collaborative recommendation [J]. Lecture Notes in Computer Science, 2006. 4198: p. 96-118.
[117] Mobasher, B., et al., Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness [J]. ACM Transactions on Internet Technology (TOIT), 2007. 7(4): 23.
[118]张富国,用户多兴趣下基于信任的协同过滤算法研究[J].小型微型计算机系统, 2008. 29(008): 1415-1419.
[119] Mehta, B., T. Hofmann, and P. Fankhauser, Lies and propaganda: detecting spam users in collaborative filtering [C]. ACM Conference on Recommender Systems, 2007, ACM. 21-28.
[120] Mehta, B., T. Hofmann, and W. Nejdl, Robust collaborative filtering [C]. ACM Conference on Recommender Systems. 2007, ACM. 49-56.
[121] Massa, P. and P. Avesani, Trust-aware collaborative filtering for recommender systems [J]. Lecture Notes in Computer Science, 2004. 3290: p. 492-508.
[122] Massa, P. and B. Bhattacharjee, Using trust in recommender systems: an experimental analysis [J]. Lecture Notes in Computer Science, 2004. 2995: 221-235.
[123] Lam, S.K. and J. Riedl, Shilling recommender systems for fun and profit [M]. 2004, ACM New York, NY, USA. 393-402.
[124] O'Mahony, M., et al., Collaborative recommendation: A robustness analysis [J]. ACM Transactions on Internet Technology (TOIT), 2004. 4(4): 344-377.
[125] Mobasher, B., et al., Effective attack models for shilling item-based collaborative filtering systems [C], WebKDD. 2005, Citeseer: Chicago, Illinois, USA. 13-23.
[126] Burke, R., et al., Classification features for attack detection in collaborative recommender systems [C], Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, ACM: Philadelphia, PA, USA. 542-547.
[127]安东尼·吉登斯,现代性的后果[M]. 2000,南京:译林出版社.
[128]许鑫,袁翀,信任模型及其改进研究[J].现代图书情报技术, 2008(002): p. 41-47.
[129] Gambetta, D., Can we trust trust. Trust: Making and Breaking Cooperative Relations [M], Department of Sociology, University of Oxford, 2000: 213-237.
[130] French, T. and K. Liu, "Hidden" semiosis: trusted security mediators [C], in The 11th International Conference on Informatics and Semiotics in Organisations. 2009: Beijing, China. 47-55.
[131]韦慧民,龙立荣,组织中人际初始信任研究述评[J].心理科学进展, 2008. 16(002):328-334.
[132] Peirce, C.S., Chance, Love, and Logic [M]. 1923: Cohen, MR, London, Kegan Paul.
[133]董才生,信任本质与类型的社会学阐释[J].河北师范大学学报:哲学社会科学版, 2004. 27(001):40-43.
[134] O'Donovan, J. and B. Smyth, Trust in recommender systems [M], in IUI. 2005, Association for Computing Machinery, Inc, One Astor Plaza, 1515 Broadway, New York, NY, 10036-5701, USA. p. 167-174.
[135]高滢,基于用户等级的协同过滤推荐算法[J].吉林大学学报:理学版, 2008. 46(003): 489-493.
[136]谢明文,关于协方差,相关系数与相关性的关系[J].数理统计与管理, 2004. 23(003):33-36.
[137] Langville, A.N. and C.D. Meyer, Deeper inside pagerank [J]. Internet Mathematics, 2004. 1(3): 335-380.
[138] Haveliwala, T., Efficient computation of PageRank [Z]. Stanford University, http://dbpubs. stanford.edu, 1999. 8090:1998-31.
[139] Kamvar, S.D., et al. Extrapolation methods for accelerating PageRank computations [M]. 2003: ACM New York, NY, USA.
[140] Brin, S. and L. Page, The anatomy of a large-scale hypertextual Web search engine [J]. Computer Networks and ISDN Systems, 1998. 30(1-7): 107-117.
[141] Sun, L. and K. Ousmanou, Articulation of information requirements for personalised knowledge construction [J]. Requirements Engineering, 2006. 11(4): 279-293.
[2]徐少春.管理软件个性化的轮回. ERP改变中国2007 [Z]; Available from: http://kingdee.e-works.net.cn/document/200708/article1926.htm.
[3]曹渝昆,基于神经网络和模糊逻辑的智能推荐系统研究[D],计算机学院. 2006,重庆大学:重庆.
[4]郭艳红,推荐系统的协同过滤算法与应用研究[D]. 2008,大连理工大学:大连.
[5] Shardanand, U. and P. Maes. Social information filtering: algorithms for automating“word of mouth”[C]. SIGCHI. 1995: ACM Press/Addison-Wesley Publishing Co.
[6] Goldberg, D., et al., Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992. 35(12): 70.
[7]黄永文,中文产品评论挖掘关键技术研究[D],计算机学院. 2009,重庆大学:重庆.
[8] Resnick, P. and H.R. Varian, Recommender systems [J]. Communications of the ACM, 1997. 40(3): 57.
[9] Al Mamunur Rashid, I.A., et al. Getting to know you: Learning new user preferences in recommender systems [M]. 2002: Citeseer.
[10] Linden, G., B. Smith, and J. York, Amazon.com recommendations: item-to-item collaborative filtering [J]. IEEE Internet computing, 2003. 7(1): 76-80.
[11] Das, A.S., et al., Google news personalization: scalable online collaborative filtering [C]. Proceedings of the 16th international conference on World Wide Web, 2007: 271-280.
[12] Di Giacomo, M., et al., MyLibrary, a personalization service for digital library environments [C]. Proceedings of the Second DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries (ERCIM Workshop Proceedings 01/W03). Dublin, 2001.
[13] Renda, M.E. and U. Straccia, A personalized collaborative Digital Library environment: a model and an application [J]. Information Processing and Management, 2005. 41(1): 5-21.
[14] Mooney, R.J. and L. Roy. Content-based book recommending using learning for text categorization [C]. Proceedings of the fifth ACM conference on Digital libraries. 2000. ACM New York, NY, USA.
[15] Li, Y., L. Lu, and L. Xuefeng, A hybrid collaborative filtering method for multiple-interestsand multiple-content recommendation in E-Commerce [J]. Expert Systems with Applications, 2005. 28(1): 67-77.
[16] Conlan, O., et al., Multi-model, metadata driven approach to adaptive hypermedia services for personalized elearning [C]. Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 2002: 100-111.
[17] Chen, C.M. and L.J. Duh, Personalized web-based tutoring system based on fuzzy item response theory [J]. Expert Systems with Applications, 2008. 34(4): 2298-2315.
[18] Fu, Y., A semiotic approach for modelling semantics in content management [D], in School of Systems Engineering. 2007, University of Reading.
[19] Mock, K.J. and V.R. Vemuri, Information filtering via hill climbing, WordNet, and index patterns [J]. Information Processing and Management, 1997. 33(5): 633-644.
[20] Sakagami, H. and T. Kamba, Learning personal preferences on online newspaper articles from user behaviors [J]. Computer Networks and ISDN Systems, 1997. 29(8-13): 1447-1455.
[21] Lai, H.J., T.P. Liang, and Y.C. Ku, Customized Internet news services based on customer profiles [C]. Proceedings of the 5th International Conference on Electronic Commerce, 2003: 225-229.
[22] Liang, T.-P., et al., A semantic-expansion approach to personalized knowledge recommendation [J]. Decision Support Systems, 2007. In Press, Corrected Proof.
[23] Chedrawy, Z. and S.S.R. Abidi. Case based reasoning for information personalization: using a context-sensitive compositional case adaptation approach [C]. Engineering of Intelligent Systems, 2006 IEEE International Conference on. 2006.
[24] Mobasher, B., Data mining for web personalization [M]. The Adaptive Web: Methods and Strategies of Web Personalization, Brusilovsky, P., Berlin Heidelberg New York: Springer Verlag, 2007.
[25] Morris, C.W., Foundations of the theory of signs [M]. 1938: The University of Chicago Press.
[26] Davidson, D. and G. Harman, Semantics of Natural Languages [J]. Philosophy of Language: The Central Topics, 2007: 57.
[27] Akmajian, A., et al., Linguistics: An introduction to language and communication [M]. 2001: The MIT press.
[28] Liu, K., Semiotics in information systems engineering [M]. 2000: Cambridge University Press.
[29] Jurafsky, D., Pragmatics and computational linguistics [M]. Handbook of Pragmatics.Blackwell, Oxford, 2002.
[30] Bunt, H. and B. Black, The ABC of computational pragmatics. Abduction Belief and Context in Dialogue [J]. Studies in Computational Pragmatics, 2000:1-46.
[31] Bunt, H. and W. Black, Abduction, belief and context in dialogue: studies in computational pragmatics [J]. Computational Linguistics, 2000. 28(1):96-98.
[32] Singh, M.P., The pragmatic web [J]. IEEE Internet computing, 2002:4-5.
[33] Singh, M.P., The pragmatic web: preliminary thoughts [C]. NSF-OntoWeb Workshop on Database and Information Systems Research for Semantic Web and Enterprises, Amicalola Falls State Park, GA, 2002.
[34]钟义信,面向智能研究的全信息理论:纪念Shannon信息论50周年[J].北京邮电大学学报, 1998. 21(004):1-6.
[35]陈彦萍,李增智,面向语用Web服务的网络服务管理机制研究[J].微电子学与计算机, 2008. 25(009): p. 31-33.
[36]翟社平,魏娟丽,李增智,基于参与者上下文概念意图的语用Web服务发现[J].计算机应用, 2009. 29(001):224-226.
[37]杨斌,易明,王学东,基于语用信息的站点个性化信息服务研究[J].情报理论与实践, 2008. 31(003): 457-460.
[38] Gori, M., et al., Itemrank: A random-walk based scoring algorithm for recommender engines [C], in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI). 2007: Hyderabad, India. p. 778-781.
[39] Montaner, M., B. Lopez, and J.L. De La Rosa, A taxonomy of recommender agents on the internet [J]. Artificial Intelligence Review, 2003. 19(4):285-330.
[40] Perugini, S. and N. Ramakrishnan, Personalizing web sites with mixed-initiative interaction [J]. IT Professional, 2003. 5(2):9-15.
[41] Riecken, D., Introduction: personalized views of personalization [J]. Communications of the ACM, 2000. 43(8):26-28.
[42] Frias-Martinez, E., et al., Automated user modeling for personalized digital libraries [J]. International Journal of Information Management, 2006. 26(3):234-248.
[43] Chiu, W. Web site personalization. 2001 [Z]; Available from: http://www.ibm.com/developerworks/websphere/library/techarticles/hipods/personalize.html.
[44] Weller, M., Virtual learning environments: using, choosing and developing your VLE. 2007, Oxon: Routledge.
[45] Zhang, Z., Community - the dawn of personalisation technology in the next generation Internet [Z]. http://googlechinablog.com/2007/08/blog-post_20.html 2007. 2008(June).
[46] Eirinaki, M. and M. Vazirgiannis, Web mining for web personalization [J]. ACM Transactions on Internet Technology, 2003. 3(1): 1-27.
[47] Murthi, B.P.S. and S. Sarkar, The role of the management sciences in research on personalization [J]. Management Science, 2003. 49(10):1344-1362.
[48] Adomavicius, G. and A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions [J]. Knowledge and Data Engineering, IEEE Transactions on, 2005. 17(6):734-749.
[49] Jeevan, V.K.J. and P. Padhi, A selective review of research in content personalization [J]. Library Review, 2006. 55(9):556-586.
[50] Montgomery, A. and M.D. Smith, Prospects for personalization on the Internet [M]. 2008: SSRN.
[51] Kim, J.K., et al., A personalized recommendation procedure for Internet shopping support [J]. Electronic Commerce Research and Applications, 2002. 1(3-4):301-313.
[52] Pazzani, M.J. and D. Billsus, Content-based recommendation systems. The Adaptive Web: Methods and Strategies of Web Personalization [J], Lecture Notes in Computer Science, 2006. 4321.
[53] Joachims, T., C. Nedellec, and C. Rouveirol. Text categorization with support vector machines: learning with many relevant [M]. 1998: Springer.
[54] Wei, C.P., C.S. Yang, and H.W. Hsiao, A collaborative filtering-based approach to personalized document clustering [J]. Decision Support Systems, 2008. 45(3):413-428.
[55] Buckley, C., et al. Automatic query expansion using SMART [C]. Overview of the Third Text Retrieval Conference (TREC 3). 1995: Diane Pub Co.
[56] Hofmann, T., Latent semantic models for collaborative filtering [J]. ACM Transactions on Information Systems (TOIS), 2004. 22(1):89-115.
[57] Hofmann, T., Probabilistic latent semantic analysis [J]. Matrix, 1999. 50: 2.
[58] Jin, X., Y. Zhou, and B. Mobasher, Web usage mining based on probabilistic latent semantic analysis [C]. Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004:197-205.
[59] Ansari, A., S. Essegaier, and R. Kohli, Internet recommendation systems [J]. Journal of Marketing Research, 2000. 37(3): 363-375.
[60] Min, S.H. and I. Han, Detection of the customer time-variant pattern for improving recommender systems [J]. Expert Systems with Applications, 2005. 28(2):189-199.
[61] Park, Y.-J. and K.-N. Chang, Individual and group behavior-based customer profile model for personalized product recommendation [J]. Expert Systems with Applications, 2008. InPress, Corrected Proof.
[62] Sarwar, B., et al., Item-based collaborative filtering recommendation algorithms [C], Proceedings of the 10th International Conference on World Wide Web. 2001: Hongkong. p. 285-295.
[63] Lemire, D. and A. Maclachlan. Slope One predictors for online rating-based collaborative filtering [M]. 2005: Society for Industrial Mathematics.
[64] Masters, J., T. Madhyastha, and A. Shakouri, ExplaNet: a collaborative learning tool and hybrid recommender system for student-authored explanations [J]. Journal of Interactive Learning Research, 2008. 19(1): 51-74.
[65]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(009):1621-1628.
[66]张锋,常会友,使用BP神经网络缓解协同过滤推荐算法的稀疏性问题[J].计算机研究与发展, 2006. 43(004): 667-672.
[67]孙小华,协同过滤系统的稀疏性与冷启动问题研究[D],计算机学院. 2005,浙江大学:杭州.
[68]李金秀,我国数字图书馆个性化信息服务探讨[J].图书馆工作与研究, 2005(003): p. 72-74.
[69] Adomavicius, G. and A. Tuzhilin, Multidimensional recommender systems: a data warehousing approach [J]. Lecture Notes in Computer Science, 2001. 2232.
[70] Morris, C.W., Signs, language and behavior. 1946: Prentice-Hall Englewood, NJ.
[71]张韧弦,形式语用学导论[M].复旦博学.语言学系列. 2008,复旦大学出版社:上海.
[72] Canhoto, A.I., Ontology-based interpretation and validation of mined knowledge: Normative and cognitive factors in data mining [C]. Data mining with ontologies: implementations, findings, and frameworks. 2007: Hershey, NY, Information Science Reference. 84-105.
[73] Liu, K. Incorporating Human Aspects into Grid Computing for Collaborative Work [C]. Keynote at ACM International Workshop on Grid Computing and e-Science. 2003. San Francisco.
[74] Liu, K. and A. Benfell, Pragmatic web– incorporating semiotics into web services [C], Keynote paper, 4th International Conference on Software and Data Technology, 26-29 July, 2009, Sofia, Bulgaria.
[75] Liu, K. Pragmatic computing: a semiotic perspective to web services [C], Keynote paper, Proceedings of ICETE2007, E-Business and Telecommunications, pp3– 15, Springer, ISSN 1865-0929.
[76] Montague, R., Pragmatics and intensional logic [J]. Dialectica, 1970. 24(4): p. 277-302.
[77]郭鸿,语用学的符号学分析[J].外语研究, 2005(005):1-4.
[78]文旭,追溯语用学的思想——语用学思想史探索之一[J].外语教学, 2003. 24(003): p. 5-10.
[79]刘根,李德华,计算语用学研究的途径和方法[J].计算机工程与应用, 2005. 41(025): 4-8.
[80]蒋严,论语用推理的逻辑属性——形式语用学初探[J].外国语:上海外国语大学学报, 2002(003):18-29.
[81] Gabbay, D.M. and J. Woods, The reach of abduction: insight and trial [M]. 2005: Elsevier Science.
[82]张大松,论科学思维的溯因推理[J].华中师范大学学报(哲社版), 1993(3):24-29.
[83] Rotter, J.B., A new scale for the measurement of interpersonal trust 1 [J]. Journal of personality, 1967. 35(4): 651-665.
[84]盛晓明,话语规则与知识基础[M]. 2000,上海:学林出版社.
[85]姚忠,吴跃,常娜,集成项目类别与语境信息的协同过滤推荐算法[J].计算机集成制造系统, 2008. 14(007):1449-1456.
[86] Chen, X., College English teaching in dynamic contexts [J]. Sino-US English Teaching, 2005. 2(11): p. 57-59.
[87]许海玲,互联网推荐系统比较研究[J].软件学报, 2009. 20(2): 350-362.
[88]李聪,梁昌勇,马丽,基于领域最近邻的协同过滤推荐算法[J].计算机研究与发展, 2008. 45(009):1532-1538.
[89]张光卫,面向场景的协同过滤推荐算法[J].系统仿真学报, 2006. 18(z2): 595-601.
[90] Canny, J. Collaborative filtering with privacy [Z]. 2002.
[91] Domingos, P. and M. Richardson, Mining the network value of customers [M]. 2001, ACM New York, NY, USA. p. 57-66.
[92]王宏宇,商务推荐系统的设计研究[D]. 2007,中国科技大学.
[93] Herlocker, J.L., et al., Evaluating collaborative filtering recommender systems [J]. ACM Transactions on Information Systems, 2004. 22(1): 5-53.
[94] Billsus, D. and M.J. Pazzani, A hybrid user model for news story classification [C]. Proceedings of the Seventh International Conference on User Modeling Table of Contents, 1999: 99-108.
[95] Sarwar, B., et al., Incremental singular value decomposition algorithms for highly scalable recommender systems [C], Fifth International Conference on Computer and Information Technology (ICCIT 2002). 2002: Shanghai, China. p. 399-404.
[96]邓爱林,左子叶,朱扬勇,基于项目聚类的协同过滤推荐算法[J].小型微型计算机系统,2004. 25(009):1665-1670.
[97]赵亮,胡乃静,个性化推荐算法设计[J].计算机研究与发展, 2002. 39(008):986-991.
[98] Cui, G., K. Liu, and Z. Yunchuan. Enhancing the agility in business processes through context-based modeling [C]. Service Operations and Logistics, and Informatics, 2008. IEEE/SOLI 2008. IEEE International Conference on. 2008. 156-164.
[99] Gao, M. and W. Zhongfu. Incorporating pragmatic information in personalized recommendation systems [C]. The 11th International Conference on Informatics and Semiotics in Organisations. 2009. p. 156-164.
[100] Dey, A.K., Understanding and using context [J]. Personal and Ubiquitous Computing, 2001. 5(1): p. 4-7.
[101] Dey, A.K. and G.D. Abowd. Towards a better understanding of context and context-Awareness [C]. CHI 2000 Workshop on the What, Who, Where, When, and How of Context-Awareness, 2000.
[102] Adomavicius, G., et al., Incorporating contextual information in recommender systems using a multidimensional approach [J]. ACM Transactions on Information Systems (TOIS), 2005. 23(1):103-145.
[103]冯在文,一种基于情境推理的语义Web服务发现方法[J].计算机学报, 2008. 31(008): 1354-1363.
[104]姜丽芬,卢桂章,辛运帏,基于RFID普适计算环境的形式化上下文模型[J].计算机工程, 2007. 33(011):173-175.
[105] Minns, A.W. and M.J. Hall, Artificial neural networks as rainfall-runoff models [J]. Hydrological Sciences Journal/Journal des Sciences Hydrologiques, 1996. 41(3): p. 399-417.
[106] Ilunga, M. and D. Stephenson, Infilling streamflow data using feed-forward back-propagation (BP) artificial neural networks: Application of standard BP and pseudo Mac Laurin power series BP techniques [M]. Water SA, 2005. 31(2):171-176.
[107]苏凌,基于径向基函数神经网络的港口吞吐量预测研究[D]. 2006,上海海事大学:上海.
[108]尹建川,径向基函数神经网络及其在船舶运动控制中的应用研究[D]. 2007,大连海事大学:大连.
[109]苏美娟,径向基函数神经网络学习算法研究[D]. 2007,苏州大学.
[110] Lemire, D. and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering [C]. Proceedings of the SIAM Data Mining Conference. 2005. Newport Beach, California: Society for Industrial Mathematics.
[111]葛哲学,孙志强,神经网络理论与MATLAB R2007实现[M]. 2007,北京:电子工业出版社.
[112]倪海燕,唐德根,语用预设的语篇功能体现[J].求索, 2005(011): p. 186-187.
[113]向长江,陈平,信任问题研究文献综述[J].广州大学学报:社会科学版, 2003. 2(005): 39-42.
[114]朱庆华,李亮,社会网络分析法及其在情报学中的应用[J].情报理论与实践, 2008. 31(002):179-183.
[115]胡勇,王陆,网络协作学习中的社会网络分析个案研究[J].开放教育研究, 2006.12(005): 56-61.
[116] Mobasher, B., et al., Analysis and detection of segment-focused attacks against collaborative recommendation [J]. Lecture Notes in Computer Science, 2006. 4198: p. 96-118.
[117] Mobasher, B., et al., Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness [J]. ACM Transactions on Internet Technology (TOIT), 2007. 7(4): 23.
[118]张富国,用户多兴趣下基于信任的协同过滤算法研究[J].小型微型计算机系统, 2008. 29(008): 1415-1419.
[119] Mehta, B., T. Hofmann, and P. Fankhauser, Lies and propaganda: detecting spam users in collaborative filtering [C]. ACM Conference on Recommender Systems, 2007, ACM. 21-28.
[120] Mehta, B., T. Hofmann, and W. Nejdl, Robust collaborative filtering [C]. ACM Conference on Recommender Systems. 2007, ACM. 49-56.
[121] Massa, P. and P. Avesani, Trust-aware collaborative filtering for recommender systems [J]. Lecture Notes in Computer Science, 2004. 3290: p. 492-508.
[122] Massa, P. and B. Bhattacharjee, Using trust in recommender systems: an experimental analysis [J]. Lecture Notes in Computer Science, 2004. 2995: 221-235.
[123] Lam, S.K. and J. Riedl, Shilling recommender systems for fun and profit [M]. 2004, ACM New York, NY, USA. 393-402.
[124] O'Mahony, M., et al., Collaborative recommendation: A robustness analysis [J]. ACM Transactions on Internet Technology (TOIT), 2004. 4(4): 344-377.
[125] Mobasher, B., et al., Effective attack models for shilling item-based collaborative filtering systems [C], WebKDD. 2005, Citeseer: Chicago, Illinois, USA. 13-23.
[126] Burke, R., et al., Classification features for attack detection in collaborative recommender systems [C], Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, ACM: Philadelphia, PA, USA. 542-547.
[127]安东尼·吉登斯,现代性的后果[M]. 2000,南京:译林出版社.
[128]许鑫,袁翀,信任模型及其改进研究[J].现代图书情报技术, 2008(002): p. 41-47.
[129] Gambetta, D., Can we trust trust. Trust: Making and Breaking Cooperative Relations [M], Department of Sociology, University of Oxford, 2000: 213-237.
[130] French, T. and K. Liu, "Hidden" semiosis: trusted security mediators [C], in The 11th International Conference on Informatics and Semiotics in Organisations. 2009: Beijing, China. 47-55.
[131]韦慧民,龙立荣,组织中人际初始信任研究述评[J].心理科学进展, 2008. 16(002):328-334.
[132] Peirce, C.S., Chance, Love, and Logic [M]. 1923: Cohen, MR, London, Kegan Paul.
[133]董才生,信任本质与类型的社会学阐释[J].河北师范大学学报:哲学社会科学版, 2004. 27(001):40-43.
[134] O'Donovan, J. and B. Smyth, Trust in recommender systems [M], in IUI. 2005, Association for Computing Machinery, Inc, One Astor Plaza, 1515 Broadway, New York, NY, 10036-5701, USA. p. 167-174.
[135]高滢,基于用户等级的协同过滤推荐算法[J].吉林大学学报:理学版, 2008. 46(003): 489-493.
[136]谢明文,关于协方差,相关系数与相关性的关系[J].数理统计与管理, 2004. 23(003):33-36.
[137] Langville, A.N. and C.D. Meyer, Deeper inside pagerank [J]. Internet Mathematics, 2004. 1(3): 335-380.
[138] Haveliwala, T., Efficient computation of PageRank [Z]. Stanford University, http://dbpubs. stanford.edu, 1999. 8090:1998-31.
[139] Kamvar, S.D., et al. Extrapolation methods for accelerating PageRank computations [M]. 2003: ACM New York, NY, USA.
[140] Brin, S. and L. Page, The anatomy of a large-scale hypertextual Web search engine [J]. Computer Networks and ISDN Systems, 1998. 30(1-7): 107-117.
[141] Sun, L. and K. Ousmanou, Articulation of information requirements for personalised knowledge construction [J]. Requirements Engineering, 2006. 11(4): 279-293.