社会网络资源在线共享与推荐方法研究
|
摘要
互联网正在由于社会网络的流行,越来越多的体现出以用户为中心的特点。社会网络应用已成Web应用的主流,为人们搭建了一个Web上资源共享与交互的平台。社会网络中,用户既是数据资源的主要创建者,也是资源的传播者,资源内容由用户自生成也是社会网络的核心理念之一。因此,由用户生成内容(User Generated Content)的社会网络环境中存在数据资源过载的问题。一方面,媒体数据和用户数据都十分庞大,而且不断有新用户加入以及每天都会大量新的数据上传；另一方面,数据资源存在无序的特点,大多数资源属于无结构化数据,如视频,图片,文本日志等,这类数据都具有多语义的特点,即每个对象包含多种类别、多粒度的语义信息,社会媒体和网页一样具备海量信息的规模,但又无法直接应用现有成熟的网页信息检索技术对其进行排序,这给社会网络中的数据资源的分类以及检索带来困难。在线共享和推荐的方式成为目前社会网络环境下数据资源传播的主要途径。
针对社会网络环境下资源共享效率以及推荐效率低下的问题,本研究探讨高效的资源共享与推荐的方法并能提升用户社交能力。本文的研究内容主要包含以下4个部分：
(1)基于用户多维特征的变粒度分类方法
社会网络以用户为中心,用户具有多特征维度的特点,特征是可用来描述用户任何性质的广泛概念,既可以是用户的个人资料,也可以是用户的兴趣爱好。从数据库角度来看,用户特征模型就是以用户为元组的用户特征项关系表,特征项就是描述用户的属性,特征是用来描述用户“属性-值”对。本文提出一种基于用户特征模型将用户从单一特征维度到多特征维度的进行变粒度分类的算法。多个用户具有相同的特征可以构成用户共同特征类,该算法能够挖掘出给定用户特征模型中满足条件的不同维度的所有用户共同特征类,特征类之间无包含关系,并且不同粒度的特征类之间建立起了层次关联。用户特征模型根据具体应用建立,算法性能和跟特征项维度,每个特征项的取值个数相关,以及用户数目等参数密切关联,本文研究分析各种参数对算法性能的影响。
(2)基于用户共同偏好自动分类的高效资源共享与推荐方法
在线共享和推荐是目前社会网络资源传播的主要途径,用户自建组机制的不足给社会网络中资源的共享与传播带来了阻碍,同时现有的推荐方法仅关注推荐的准确率,忽视了考虑推荐效率。本文提出了一种基于用户偏好的组自动生成方法。与用户手动创建的组进行区分,我们把本文提出的基于用户偏好的系统自动生成组称为“共同偏好组(Common Preference Group),简称为CPG”。社会网络中,用户对一个资源对象感兴趣是因为用户对媒体对象所蕴含的兴趣元素感兴趣。因此,我们把用户对许多单一资源的喜好转化为用户对兴趣元素的集合的兴趣。每个用户对每个兴趣元素都可以有自身的喜好程度,我们把同一兴趣元素上的一种喜好程度称为偏好。如果一系列用户对若干兴趣元素上的每个主题都具有相同的喜好程度,我们则称这群用户在这些兴趣元素上具有共同的偏好,我们把那些有共同偏好的用户聚集成一个组,也就是我们提到的共同偏好组。本文提出了基于共同偏好组的资源共享和推荐系统架构,然后设计实现了CPG自动生成算法,并提出了基于CPG协同兴趣发现的思想和方法,本文还研究了资源对象与CPG推荐匹配策略。
(3)共同偏好组近似批量更新方法
社会网络中存在海量用户,用户的注销,新用户的加入以及用户兴趣发生变化都会使得用户偏好模型发生改变。每当用户偏好模型发生改变时可以重新进行CPG挖掘算法来得到时新的CPG,但每次都对CPG重新挖掘的代价太大。本文先总结用户行为如何引起用户偏好模型的变化,并分析了用户偏好模型变化引起共同偏好CPG变化的各类情况,然后提出了共同偏好组近似批量更新方法,利用这种方法可以对CPG定时批量更新,不需要对所有用户重新挖掘,实现高效更新目标。
(4)共同偏好组在Web社区管理系统中的应用
社会网络中存在大量社区,并呈快速增长趋势。用户在社会网络中的行为,如上传资源,评论,讨论等等都能揭示用户的关注的兴趣元素。如何本研究探讨如何把共同偏好组方法体系运用到Web社区管理系统中,设计开发Web社区高级应用功能,如发现社区中兴趣相投的用户、协助社区组建、社区资源的多样性推荐和惊喜推荐等功能。
With the advent of Web2.0, users are allowed to produce content in the Web. The social Web is a set of relationships that link together people over the World Wide Web. Typical Social Web applications which include social networking services, social media and online communities, etc. have already become the mainstream of web application. User interaction is a pivotal characteristic of the social Web. Starting with the social media sites, such as Flickr and Youtube, user-generated contents have taken over social web. Users and contents in these sites are still rapidly increasing every day. Traditional information retrieval technologies have limits when dealing with the problem of information overload in the social Web, mainly in that large amounts of unstructured data produced by users is more difficult to classify and retrieve. In the circumstance of massive user-generated unstructured data, data sharing and recommendation approaches take a more important role than information retrieval approaches for data diffusion in the Social Web.
This thesis focuses on efficient resourse sharing and recommendation. Research contents and innovations of the thesis are summarized as follows:
(1) A variable granularity user classification algorithm based on multi-dimensional features of users
Classifying Web users based on multi-dimensional features is one of the foundations of realizing personalized Web applications. It could be used for user classification model, users' multi-dimensional data analysis, potential user group discovery and personalized recommendation and so forth. This paper proposes a variable granularity user classification algorithm based on the user feature model, which classifying the users from the single feature dimension to the multiple-features dimension. The essential idea is to gather those users who have the same features in a given feature space. The proposed algorithm has the following characteristic:a) all the qualified common feature categories could be mined; b) there is no inclusion relationship between any two mined categories; c) the common feature categories with different granularity are organized with the hierarchical structure. Variable granularity classification of web users based on their features could benefit many Web applications, such as multi-dimensional users' data analysis, potential community discovering, user classification model, and personalized recommendation services.
(2) Towards an efficient data sharing and recommendation approach based on the common preference group.
In the circumstance of massive user-generated unstructured data, data sharing and recommendation approaches take a more important role than information retrieval approaches for data diffusion in the Social Web. In this thesis, we propose a new approach to discover groups automatically based on user's preference. We call the group which is automatically generating by our approach the Common Preference Group (CPG). This research is conducting under a hypothesis that a user like a data object because the user is interested in some semantic topics implied in the object. With this assumption, we switch interests of users from the objects to the semantic topics, and we group users who share common interests together as a CPG.
The automatic grouping approach has the following features:i) a CPG is corresponding to a set of semantic topics, which indicate the users'preference about each semantic topic; ii) the CPG is automatically generated, and a user whose preference matches a CPG will be added to this CPG automatically; iii) The data object can be automatically added to the corresponding CPG pools as well. CPG can be used as social purposes and data recommendation. Compared with current group mechanism, the new features of CPG bring these advantages:i) the users could discover their own preferences and the other people who have the same preference with them; ii) different CPG can be recommended to users based on user's preferences; iii) objects are distributed to different CPG without human involved.
(3) An approximate approach for batch update of common preference group
User's preferences are not static, so the UPM is changing from time to time. With massive users, each time the user preference model changed, mining CPG from the scratch is not an option. In this paper, we analyzed the types of user behavior and their corresponding modification of UPM, and then we summarized how the two types of UPM modifications influenced CPG. We proposed an approximate batch CPG update approach to keep them up to date and also avoid CPG re-computing.
(4) Implementation of CPG based advanced functions in the Web community system
There exsit a large amount of Web communities in social Web and the number are still increasing rapidly. The user behaviors, such as upload resources or post a comment, reveal user's preference. We developed a Web community management system prototype and provided a series of advanced functions for Web communities, services and data management based on TOTEM Object Deputy Database System. In the thesis, we explore to realize many new advanced functions based CPG in our Web community management system prototype.
针对社会网络环境下资源共享效率以及推荐效率低下的问题,本研究探讨高效的资源共享与推荐的方法并能提升用户社交能力。本文的研究内容主要包含以下4个部分：
(1)基于用户多维特征的变粒度分类方法
社会网络以用户为中心,用户具有多特征维度的特点,特征是可用来描述用户任何性质的广泛概念,既可以是用户的个人资料,也可以是用户的兴趣爱好。从数据库角度来看,用户特征模型就是以用户为元组的用户特征项关系表,特征项就是描述用户的属性,特征是用来描述用户“属性-值”对。本文提出一种基于用户特征模型将用户从单一特征维度到多特征维度的进行变粒度分类的算法。多个用户具有相同的特征可以构成用户共同特征类,该算法能够挖掘出给定用户特征模型中满足条件的不同维度的所有用户共同特征类,特征类之间无包含关系,并且不同粒度的特征类之间建立起了层次关联。用户特征模型根据具体应用建立,算法性能和跟特征项维度,每个特征项的取值个数相关,以及用户数目等参数密切关联,本文研究分析各种参数对算法性能的影响。
(2)基于用户共同偏好自动分类的高效资源共享与推荐方法
在线共享和推荐是目前社会网络资源传播的主要途径,用户自建组机制的不足给社会网络中资源的共享与传播带来了阻碍,同时现有的推荐方法仅关注推荐的准确率,忽视了考虑推荐效率。本文提出了一种基于用户偏好的组自动生成方法。与用户手动创建的组进行区分,我们把本文提出的基于用户偏好的系统自动生成组称为“共同偏好组(Common Preference Group),简称为CPG”。社会网络中,用户对一个资源对象感兴趣是因为用户对媒体对象所蕴含的兴趣元素感兴趣。因此,我们把用户对许多单一资源的喜好转化为用户对兴趣元素的集合的兴趣。每个用户对每个兴趣元素都可以有自身的喜好程度,我们把同一兴趣元素上的一种喜好程度称为偏好。如果一系列用户对若干兴趣元素上的每个主题都具有相同的喜好程度,我们则称这群用户在这些兴趣元素上具有共同的偏好,我们把那些有共同偏好的用户聚集成一个组,也就是我们提到的共同偏好组。本文提出了基于共同偏好组的资源共享和推荐系统架构,然后设计实现了CPG自动生成算法,并提出了基于CPG协同兴趣发现的思想和方法,本文还研究了资源对象与CPG推荐匹配策略。
(3)共同偏好组近似批量更新方法
社会网络中存在海量用户,用户的注销,新用户的加入以及用户兴趣发生变化都会使得用户偏好模型发生改变。每当用户偏好模型发生改变时可以重新进行CPG挖掘算法来得到时新的CPG,但每次都对CPG重新挖掘的代价太大。本文先总结用户行为如何引起用户偏好模型的变化,并分析了用户偏好模型变化引起共同偏好CPG变化的各类情况,然后提出了共同偏好组近似批量更新方法,利用这种方法可以对CPG定时批量更新,不需要对所有用户重新挖掘,实现高效更新目标。
(4)共同偏好组在Web社区管理系统中的应用
社会网络中存在大量社区,并呈快速增长趋势。用户在社会网络中的行为,如上传资源,评论,讨论等等都能揭示用户的关注的兴趣元素。如何本研究探讨如何把共同偏好组方法体系运用到Web社区管理系统中,设计开发Web社区高级应用功能,如发现社区中兴趣相投的用户、协助社区组建、社区资源的多样性推荐和惊喜推荐等功能。
With the advent of Web2.0, users are allowed to produce content in the Web. The social Web is a set of relationships that link together people over the World Wide Web. Typical Social Web applications which include social networking services, social media and online communities, etc. have already become the mainstream of web application. User interaction is a pivotal characteristic of the social Web. Starting with the social media sites, such as Flickr and Youtube, user-generated contents have taken over social web. Users and contents in these sites are still rapidly increasing every day. Traditional information retrieval technologies have limits when dealing with the problem of information overload in the social Web, mainly in that large amounts of unstructured data produced by users is more difficult to classify and retrieve. In the circumstance of massive user-generated unstructured data, data sharing and recommendation approaches take a more important role than information retrieval approaches for data diffusion in the Social Web.
This thesis focuses on efficient resourse sharing and recommendation. Research contents and innovations of the thesis are summarized as follows:
(1) A variable granularity user classification algorithm based on multi-dimensional features of users
Classifying Web users based on multi-dimensional features is one of the foundations of realizing personalized Web applications. It could be used for user classification model, users' multi-dimensional data analysis, potential user group discovery and personalized recommendation and so forth. This paper proposes a variable granularity user classification algorithm based on the user feature model, which classifying the users from the single feature dimension to the multiple-features dimension. The essential idea is to gather those users who have the same features in a given feature space. The proposed algorithm has the following characteristic:a) all the qualified common feature categories could be mined; b) there is no inclusion relationship between any two mined categories; c) the common feature categories with different granularity are organized with the hierarchical structure. Variable granularity classification of web users based on their features could benefit many Web applications, such as multi-dimensional users' data analysis, potential community discovering, user classification model, and personalized recommendation services.
(2) Towards an efficient data sharing and recommendation approach based on the common preference group.
In the circumstance of massive user-generated unstructured data, data sharing and recommendation approaches take a more important role than information retrieval approaches for data diffusion in the Social Web. In this thesis, we propose a new approach to discover groups automatically based on user's preference. We call the group which is automatically generating by our approach the Common Preference Group (CPG). This research is conducting under a hypothesis that a user like a data object because the user is interested in some semantic topics implied in the object. With this assumption, we switch interests of users from the objects to the semantic topics, and we group users who share common interests together as a CPG.
The automatic grouping approach has the following features:i) a CPG is corresponding to a set of semantic topics, which indicate the users'preference about each semantic topic; ii) the CPG is automatically generated, and a user whose preference matches a CPG will be added to this CPG automatically; iii) The data object can be automatically added to the corresponding CPG pools as well. CPG can be used as social purposes and data recommendation. Compared with current group mechanism, the new features of CPG bring these advantages:i) the users could discover their own preferences and the other people who have the same preference with them; ii) different CPG can be recommended to users based on user's preferences; iii) objects are distributed to different CPG without human involved.
(3) An approximate approach for batch update of common preference group
User's preferences are not static, so the UPM is changing from time to time. With massive users, each time the user preference model changed, mining CPG from the scratch is not an option. In this paper, we analyzed the types of user behavior and their corresponding modification of UPM, and then we summarized how the two types of UPM modifications influenced CPG. We proposed an approximate batch CPG update approach to keep them up to date and also avoid CPG re-computing.
(4) Implementation of CPG based advanced functions in the Web community system
There exsit a large amount of Web communities in social Web and the number are still increasing rapidly. The user behaviors, such as upload resources or post a comment, reveal user's preference. We developed a Web community management system prototype and provided a series of advanced functions for Web communities, services and data management based on TOTEM Object Deputy Database System. In the thesis, we explore to realize many new advanced functions based CPG in our Web community management system prototype.
引文
[1]汤代禄,韩建俊,边振兴.互联网的变革：Web 2.0理念与设计[M].电子工业出版社,2007.
[2]曾承,吕金虎,洪亮,贾大文.网络化社交媒体的传播复杂性.中国计算机学会通讯,2012,8(4)：25-31.
[3]Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The PageRank citation ranking: Bringing order to the Web. Technical Report, Stanford University Database Group,1998.
[4]Paul Graham (November 2005). "Web 2.0". http://www.paulgraham.com/web20.html. Retrieved Apr. 2013.
[5]T. Hofmann, "Latent semantic models for collaborative filtering," ACM Transactions on Information Systems, vol.22, no. 1,pp.89-115,2004.
[6]K. Miyahara and M. J. Pazzani, "Collaborative filtering with the simple Bayesian classifier," in Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, pp.679-689,2000.
[7]X. Su and T. M. Khoshgoftaar, "Collaborative filtering for multi-class data using belief nets algorithms," in Proceedings of the International Conference on Tools with Artificial Intelligence (ICTAI '06), pp.497-504,2006.
[8]Lops, Pasquale, Marco de Gemmis, and Giovanni Semeraro. "Content-based recommender systems: State of the art and trends." Recommender Systems Handbook. Springer US,2011.73-105..
[9]Appelquist D, Brickley D, Carvahlo M, et al. (2010) A Standards-based, Open and Privacy aware Social Web. W3C Incubator Group Report.
[10]P. DeRose, W. Shen, F. Chen, A. Doan,R. Ramakrishnan. Building structured Web community portals: A top-down, compositional, and incremental approach. In VLDB 2007.
[11]Nilesh Bansal, Nick Koudas, BlogScope:A System for Online Analysis of High Volume Text Streams, In Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, Vienna, Austria, Sept 23-28 2007, Demonstration Proposal.
[12]何镝.彭智勇.梅晓茸Web社区管理研究综述.计算机科学与探索2011(2).
[13]Jovanovic', Jelena; Dragan Gas 'evic', Carlo Torniaic, Scott Batemand, Marek Hatalae (4 Dec 2009). "The Social Semantic Web in Intelligent Learning Environments:state of the art and future challenges". Routledge 14 (4):273-309.
[14]G. Shani, D. Heckerman, and R. I. Brafman, "An MDP-based recommender system," Journal of Machine Learning Research,vol.6, pp.1265-1295,2005.
[15]"Facebook:One Social Graph to Rule Them All?". CBS News. Retrieved Apr.11,2013.
[16]L. H. Ungar and D. P. Foster, "Clustering methods for collaborative filtering," in Proceedings of the Workshopon Recommendation Systems, AAAI Press,1998.
[17]Linyuan Lu, Duan-Bing Chen and Tao Zhou, The small world yields the most effective information spreading, New J Phys 13 (2011) 123005.
[18]"How the Interest Graph will shape the future of the web", MIT Entrepreneurship Review, Cambridge, 1 April 2011. Retrieved on 11 March 2013.
[19]S. H. S. Chee, J. Han, and K. Wang, "RecTree:an efficient collaborative filtering method," in Proceedings of the 3rd International Conference on Data Warehousing and Knowledge Discovery, pp.141-151,2001.
[20]Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Introduction to recommender systems handbook." Recommender Systems Handbook. Springer US,2011.1-35.
[21]Adomavicius, G.; Tuzhilin, A. (June 2005). "Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions". IEEE Transactions on Knowledge and Data Engineering 17 (6):734-749.
[22]Herlocker, J. L.; Konstan, J. A.; Terveen, L. G.; Riedl, J. T. (2004). "Evaluating collaborative filtering recommender systems". ACM Trans. Inf. Syst.22 (1):5-53.
[23]Prem Melville and Vikas Sindhwani, Recommender Systems, Encyclopedia of Machine Learning, 2010.
[24]http://yogar.blogbus.com/logs/62725127.html. Retrieved in Apr.20,2013.
[25]Koren, Y., Bell, R.M., Volinsky, C.:Matrix factorization techniques for recommender systems. IEEE Computer 42(8),30-37 (2009).
[26]Konstan, J. A., B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM,40(3):77-87,1997.
[27]Konstan, J. A., J. Riedl, A. Borchers, and J. L. Herlocker. Recommender systems:a GroupLens perspective. In Recommender Systems. Papers from 1998 Workshop. Technical Report WS-98-08. AAAI Press,1998.
[28]Negoescu R-A and Gatica-Perez D (2008) Analyzing Flickr groups. In:Conference on Image and Video Retrieval.
[29]Zheng N., Li D., Liao S., Zhang L.:Flickr group recommendation based on tensor decomposition. In: In:Proceedings of the 33nd ACM SIGIR Conference, pp:737-738 (2010).
[30]Lerman K, Jones L. Social Browsing on Flickr. Proceedings of International Conference on Weblogs and Social Media. Boulder, Colorado, USA,2007. Palo Alto:AAAI,2007:210-230.
[31]Negoescu R A, Gatica-Perez D. Topickr:flickr groups and users reloaded. Proceedings of the 16th ACM International Conference on Multimedia. Vancouver, Canada,2008. New York:ACM,2008: 857-860.
[32]Cai Jun-Jie, Zha Zheng-Jun, Qi Tian, Wang Zeng-Fu. Semi-automatic Flickr Group Suggestion. Proceedings of the 17th International Multimedia Modeling Conference. Taibei, Taiwan,2011. Berlin: Springer,2011:77-87.
[33]Chen Hong-Ming, Chang Ming-Hsiu, Chang Ping-Chieh, Tien Ming-Chun, Winston H. Hsu, Wu Ja-Ling. SheepDog:group and tag recommendation for flickr photos by automatic search-based learning. Proceedings of the 16th ACM international conference on Multimedia. Vancouver, British Columbia, Canada,2008. New York:ACM,2008:737-740.
[34]Zheng Nan, Li Qiu-Dan, Liao Sheng-Cai, Zhang Lei-Ming. Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr. Journal of Information Science,2010,36(6): 733-750.
[35]Zwol R V. Flickr:Who is Looking?. Proceedings of ACM International Conference on Web Intelligence. Silicon Valley, California, USA,2007.
[36]Yu J., Jin X., Han J., Luo J.:Collection-based sparse label propagation and its application on social group suggestion from photos. Journal ACM Transactions on Intelligent Systems and Technology (TIST),Vol.2, Issue 2. ACM, New York (2011).
[37]Burke, R.:Hybrid web recommender systems. In:The AdaptiveWeb, pp.377-408. Springer Berlin/ Heidelberg (2007).
[38]Bu Jia-Jun, Tan Shu-Long, Chen Chun, Wang Can, Wu Hao, Zhang Li-Jun, He Xiao-Fei. Music recommendation by unified hypergraph:combining social media information and music content. Proceedings of the 18th ACM International Conference on Multimedia. Philadelphia, Pennsylvania,2010. New York:ACM,2010:391-400.
[39]Mooney R J, Roy L. Content-based book recommending using learning for text categorization. Proceedings of the 5th ACM Conference on Digital Libraries. San Antonio, USA,2000. New York:ACM, 2010:195-204.
[40]Xiaoyuan Su and Taghi M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, vol.2009, Article ID 421425,19 pages,2009. doi:10.1155/2009/421425
[41]Loannis K,Stathopoulos V, Jose J M. On social networks and collaborative recommendation. Proceedings of the 32nd ACM Special Interest Group on Information Retrieval. Boston, USA,2009. New York:ACM,2009:195-202.
[42]Mahmood T, Ricci F. Towards learning user-adaptive state models in a conversational recommender system. Proceedings of the 15th Workshop on Adaptivity and User Modeling in Interactive Systems. Halle, Germany,2007. Halle:Martin Luther University Press,2007:373-378.
[43]Ricci,F., Cavada, D., Mirzadeh, N., Venturini, A.:Case-based travel recommendations. In:D.R. Fesenmaier, K.Woeber, H.Werthner (eds.) Destination Recommendation Systems:Behavioural Foundations and Applications, pp.67-93. CABI (2006).
[44]Bridge, D., G" oker, M., McGinty, L., Smyth, B.:Case-based recommender systems. The Knowledge Engineering review 20(3),315-320 (2006).
[45]Arazy, O., Kumar, N., Shapira, B.:Improving social recommender systems. IT Professional 11 (4),38-44 (2009).
[46]Ben-Shimon, D., Tsikinovsky, A., Rokach, L., Meisels, A., Shani, G., Naamani, L.:Recommender system from personal social networks. In:K. Wegrzyn-Wolska, P.S. Szczepaniak (eds.) AWIC, Advances in Soft Computing, vol.43, pp.47-55. Springer (2007).
[47]Yoshii K, Goto M, Komatani K, Ogata T, Okuno H G. Hybrid Collaborative and Content-based Music Recommendation Using Probabilistic Model with Latent User Preferences. Proceedings of the 7th International Conference on Music Information Retrieval. Victoria, BC, Canada,2006:296-301.
[48]Basilico J, Hofmann T. Unifying collaborative and content-based filtering. Proceedings of the Twenty-first International Conference on Mechine Learning. Banff, Alberta, Canada,2004. New York: ACM,2004:65-72.
[49]N. Good, J. B. Schafer, J. A. Konstan, A. Borchers, B. M. Sarwar, J. L. Herlocker, and J. Riedl. Combining collaborative filtering with personal agents for better recommendations. In AAAI/IAAI, pages 439-446,1999.
[50]J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and de-velopment in information retrieval, pages 230-237. ACM Press,1999.
[51]Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens:An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 Computer Supported Cooperative Work Conference,1994.
[52]M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Infor-mation Systems,22:143-177,2004.
[53]G. Linden, B. Smith, and J. York, "Amazon.com recommendations:item-to-item collaborative filtering," IEEE Internet Computing, vol.7, no.1, pp.76-80,2003.
[54]Sarwar B M, Karypis G, Konstan J A, Riedl J. Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th ACM International World Wide Web Conference. Hong Kong, China, 2001. New York:ACM,2001:285-295.
[55]Popescul, A., L. H. Ungar, D. M. Pennock, and S. Lawrence. Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In Proc. of the 17th Conf. on Uncertainty in Artificial Intelligence, Seattle, WA,2001.
[56]Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock (2002). "Methods and Metrics for Cold-Start Recommendations". Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002). New York City, New York:ACM. pp.253-260.
[57]M. Balabanovic and Y. Shoham, "Content-based, collaborative recommendation,"Communications of the ACM, vol.40, no.3, pp.66-72,1997.
[58]D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles, "Collaborative filtering by personality diagnosis:a hybrid memory-and model-based approach," in Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI'00), pp.473-480,2000.
[59]Y. Xie, V.V. Phoha, Web user clustering from access log using belief function, in:Proceedings of the First International Conference on Knowledge Capture (K-CAP 2001), ACM Press,2001, pp.202-208.
[60]J. Xiao, Y. Zhang, X. Jia, and T. Li. Measuring Similarity of Interests for Clustering Web-Users. In Proc. of the 12th Australasian Conf. on Database Technologies, pages 107-114, Queensland, Australia, 2001.
[61]Rheingold, H. (2000). The Virtual Community:Homesteading on the Electronic Frontier. London: MIT Press.
[62]M. Girvan and M. E. J. Newman. Community structure in social and biological networks, volume 99, pages 7821-7826,2002.
[63]J. Huang, H. Sun, J. Han, H. Deng, Y. Sun, and Y. Liu. Shrink:a structural clustering algorithm for detecting hierarchical communities in networks. In CIKM, pages 219-228. ACM,2010.
[64]G. Palla, I. Derenyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature,435(7043):814-818,2005.
[65]S. White and P. Smyth. A spectral clustering approach to finding communities in graph. In SDM,2005.
[66]Jon K. Authoritative sources in hyperlinked environment[J]. Journal of the ACM,1999,46(5): 604-632.
[67]Kumar R, Raghavan P, Rajagopalan S, et al. Trawling emerging cyber communities automatically[C]. Proceedings of the 8th International Conference on World Wide Web,1999:1481-1493.
[68]Kumar R, Raghavan P, Rajagopalan S, et al. Extracting large-scale knowledge bases from the web[C]. Proceedings of the 25th International Conference on Very Large Data Bases,1999:639-650.
[69]Flake G W, Lawrence S, Giles C L. Efficient identification of web communities[C]. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2000:150-160.
[70]Miller M, Lynn S, James C. Birds of a feather:Homophily in social networks[J]. Annual Review of Sociology,2001,27(1):415-444.
[71]Golder S, Huberman B. Usage patterns of collaborative tagging system[J]. Journal of Information Science,2006,32(2):198-208.
[72]Li X, Guo L, Zhao Y. Tag-based social interest discovery. Proceedings of the 17th International Conference on World Wide Web,2008:675-684.
[73]Adamic L, Adar E. Friends and neighbors on the web[J]. Social Networks,2003,25(3):211-230.
[74]Matsuo Y, Mori J, Hamasaki M, et al. POLYPHONET:An advanced social network extraction system from the web. Proceedings of the 15th International Conference on World Wide Web,2006:397-406.
[75]Zheleva E, Getoor L. To join or not to join:The illusion of privacy in social networks with mixed public and private user profiles. Proceedings of the 18th International Conference on World Wide Web, 2009:531-540.
[76]Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp:745-752.
[77]H.-P. Kriegel, P. Kroger, and A. Zimek, "Clustering high-dimensional data:A survey on subspace clustering, patternbased clustering, and correlation clustering," TKDD, vol.3, no.1,2009.
[78]M. Pennacchiotti and A.-M. Popescu, "Democrats, republicans and starbucks afficionados:user classification in Twitter," in Proc. ACM KDD 2011, pp.430-438.
[79]Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). Foundations of Machine Learning, The MIT Press.
[80]高文(2013).网络视频服务的关键问题.中国计算机学会通讯,第9卷,第1期.
[81]Cui Bin, Tung A K H, Zhang Ce, Zhao Zhe. Multiple feature fusion for social media applications. Proceedings of ACM Special Interest Group on Management of Data. Indianapolis, Indiana, USA,2010. New York:ACM,2010:435-446.
[82]项亮(2012).推荐系统实践.人民邮电出版社.
[83]Nilesh Bansal, Nick Koudas, BlogScope:A System for Online Analysis of High Volume Text Streams, In Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, Vienna, Austria, Sept 23-28 2007, Demonstration Proposal.
[84]Tomoko Murakami, Koichiro Mori, and Ryohei Orihara. Metrics for evaluating the serendipity of recommendation lists. New Frontiers in Artificial Intelligence,4914:40-46,2008.
[85]Pasquier N, Bastide Y, Taouil R, Lakhal L. Discovering frequent closed itemsets for association rules. Proceeding of the 7th International Conference on Database Theory. Jerusalem, Israel,1999. Berlin: Springer,1999:398-416.
[86]Zaki M J, Parthasarathy S, Ogihara M, and Li Wei. New Algorithms for Fast Discovery of Association Rules. Proceeding of 3rd International Conference on Knowledge Discovery and Data Mining. Newport Beach, USA,1997. Menlo Park:AAAI Press,1997:283-296
[87]Borgelt C. Efficient Implementations of Apriori and Eclat. Proceeding of IEEE International Conference on Data Mining Workshop Frequent Itemset Mining Implementations. Melbourne, USA, 2003. Aachen:CEUR-WS.org,2003:vol.80.
[88]Cheng Zeng, Haiyang Zhou, Bing Yan:Cross-Media Database Retrieval System Based on TOTEM. WISE Workshops 2006:182-193
[89]Zhiyong Peng, Yuan Shi, Boxuan Zhai:Realization of Biological Data Management by Object Deputy Database System.:49-67(2006)
[90]Zhiyong Peng, Qing Li, Ling Feng, Xuhui Li, Junqiang Liu:Using Object Deputy Model to Prepare Data for Data Warehousing. IEEE Trans. Knowl. Data Eng.17(9):1274-1288 (2005)
[91]Zhiyong Peng, Yahiko Kambayashi:Deputy Mechanisms for Object-Oriented Databases. ICDE 1995: 333-340
[92]Duncan J W, Steven H S. Collective dynamics of smallworld networks[J]. Nature,1998,393:440-442.
[93]Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science,1999,286:509-512.
[94]Tantipathananandh C, Berger-Wolf T, Kempe D. A framework for community identification in dynamic social networks, in ope:Parameter-free mining of large timeevolving graphs[C]. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2007:717-726.
[95]Chi Y, Zhu S, Song X, et al. Structural and temporal analysis of the blogosphere through community factorization[C]. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD),2007:163-172.
[96]Lin Y, Chi Y, Zhu S, et al. FacetNet:A framework for analyzing communities and their evolutions in dynamic networks[C]. Proceedings of the 17th International Conference on World Wide Web,2008:685-694.
[98]Hyeongon W, Seungjin O, Jungtae M, et al. A team formation model based on knowledge and collaboration[J]. Expert Systems with Applications,2009,36(5):9121-9134.
[99]Lappas T, Liu K, Terzi E. Finding a team of experts in social networks[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2009:467-476.
[100]A. Anagnostopoulos, L. Becchetti, C. Castillo, A. Gionis, and S. Leonardi. Online team formation in social networks. In Proc. of the International World Wide Web Conference (WWW 2012), pages 839-848, Lyon, France,2012. ACM.
[2]曾承,吕金虎,洪亮,贾大文.网络化社交媒体的传播复杂性.中国计算机学会通讯,2012,8(4)：25-31.
[3]Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The PageRank citation ranking: Bringing order to the Web. Technical Report, Stanford University Database Group,1998.
[4]Paul Graham (November 2005). "Web 2.0". http://www.paulgraham.com/web20.html. Retrieved Apr. 2013.
[5]T. Hofmann, "Latent semantic models for collaborative filtering," ACM Transactions on Information Systems, vol.22, no. 1,pp.89-115,2004.
[6]K. Miyahara and M. J. Pazzani, "Collaborative filtering with the simple Bayesian classifier," in Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, pp.679-689,2000.
[7]X. Su and T. M. Khoshgoftaar, "Collaborative filtering for multi-class data using belief nets algorithms," in Proceedings of the International Conference on Tools with Artificial Intelligence (ICTAI '06), pp.497-504,2006.
[8]Lops, Pasquale, Marco de Gemmis, and Giovanni Semeraro. "Content-based recommender systems: State of the art and trends." Recommender Systems Handbook. Springer US,2011.73-105..
[9]Appelquist D, Brickley D, Carvahlo M, et al. (2010) A Standards-based, Open and Privacy aware Social Web. W3C Incubator Group Report.
[10]P. DeRose, W. Shen, F. Chen, A. Doan,R. Ramakrishnan. Building structured Web community portals: A top-down, compositional, and incremental approach. In VLDB 2007.
[11]Nilesh Bansal, Nick Koudas, BlogScope:A System for Online Analysis of High Volume Text Streams, In Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, Vienna, Austria, Sept 23-28 2007, Demonstration Proposal.
[12]何镝.彭智勇.梅晓茸Web社区管理研究综述.计算机科学与探索2011(2).
[13]Jovanovic', Jelena; Dragan Gas 'evic', Carlo Torniaic, Scott Batemand, Marek Hatalae (4 Dec 2009). "The Social Semantic Web in Intelligent Learning Environments:state of the art and future challenges". Routledge 14 (4):273-309.
[14]G. Shani, D. Heckerman, and R. I. Brafman, "An MDP-based recommender system," Journal of Machine Learning Research,vol.6, pp.1265-1295,2005.
[15]"Facebook:One Social Graph to Rule Them All?". CBS News. Retrieved Apr.11,2013.
[16]L. H. Ungar and D. P. Foster, "Clustering methods for collaborative filtering," in Proceedings of the Workshopon Recommendation Systems, AAAI Press,1998.
[17]Linyuan Lu, Duan-Bing Chen and Tao Zhou, The small world yields the most effective information spreading, New J Phys 13 (2011) 123005.
[18]"How the Interest Graph will shape the future of the web", MIT Entrepreneurship Review, Cambridge, 1 April 2011. Retrieved on 11 March 2013.
[19]S. H. S. Chee, J. Han, and K. Wang, "RecTree:an efficient collaborative filtering method," in Proceedings of the 3rd International Conference on Data Warehousing and Knowledge Discovery, pp.141-151,2001.
[20]Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Introduction to recommender systems handbook." Recommender Systems Handbook. Springer US,2011.1-35.
[21]Adomavicius, G.; Tuzhilin, A. (June 2005). "Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions". IEEE Transactions on Knowledge and Data Engineering 17 (6):734-749.
[22]Herlocker, J. L.; Konstan, J. A.; Terveen, L. G.; Riedl, J. T. (2004). "Evaluating collaborative filtering recommender systems". ACM Trans. Inf. Syst.22 (1):5-53.
[23]Prem Melville and Vikas Sindhwani, Recommender Systems, Encyclopedia of Machine Learning, 2010.
[24]http://yogar.blogbus.com/logs/62725127.html. Retrieved in Apr.20,2013.
[25]Koren, Y., Bell, R.M., Volinsky, C.:Matrix factorization techniques for recommender systems. IEEE Computer 42(8),30-37 (2009).
[26]Konstan, J. A., B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM,40(3):77-87,1997.
[27]Konstan, J. A., J. Riedl, A. Borchers, and J. L. Herlocker. Recommender systems:a GroupLens perspective. In Recommender Systems. Papers from 1998 Workshop. Technical Report WS-98-08. AAAI Press,1998.
[28]Negoescu R-A and Gatica-Perez D (2008) Analyzing Flickr groups. In:Conference on Image and Video Retrieval.
[29]Zheng N., Li D., Liao S., Zhang L.:Flickr group recommendation based on tensor decomposition. In: In:Proceedings of the 33nd ACM SIGIR Conference, pp:737-738 (2010).
[30]Lerman K, Jones L. Social Browsing on Flickr. Proceedings of International Conference on Weblogs and Social Media. Boulder, Colorado, USA,2007. Palo Alto:AAAI,2007:210-230.
[31]Negoescu R A, Gatica-Perez D. Topickr:flickr groups and users reloaded. Proceedings of the 16th ACM International Conference on Multimedia. Vancouver, Canada,2008. New York:ACM,2008: 857-860.
[32]Cai Jun-Jie, Zha Zheng-Jun, Qi Tian, Wang Zeng-Fu. Semi-automatic Flickr Group Suggestion. Proceedings of the 17th International Multimedia Modeling Conference. Taibei, Taiwan,2011. Berlin: Springer,2011:77-87.
[33]Chen Hong-Ming, Chang Ming-Hsiu, Chang Ping-Chieh, Tien Ming-Chun, Winston H. Hsu, Wu Ja-Ling. SheepDog:group and tag recommendation for flickr photos by automatic search-based learning. Proceedings of the 16th ACM international conference on Multimedia. Vancouver, British Columbia, Canada,2008. New York:ACM,2008:737-740.
[34]Zheng Nan, Li Qiu-Dan, Liao Sheng-Cai, Zhang Lei-Ming. Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr. Journal of Information Science,2010,36(6): 733-750.
[35]Zwol R V. Flickr:Who is Looking?. Proceedings of ACM International Conference on Web Intelligence. Silicon Valley, California, USA,2007.
[36]Yu J., Jin X., Han J., Luo J.:Collection-based sparse label propagation and its application on social group suggestion from photos. Journal ACM Transactions on Intelligent Systems and Technology (TIST),Vol.2, Issue 2. ACM, New York (2011).
[37]Burke, R.:Hybrid web recommender systems. In:The AdaptiveWeb, pp.377-408. Springer Berlin/ Heidelberg (2007).
[38]Bu Jia-Jun, Tan Shu-Long, Chen Chun, Wang Can, Wu Hao, Zhang Li-Jun, He Xiao-Fei. Music recommendation by unified hypergraph:combining social media information and music content. Proceedings of the 18th ACM International Conference on Multimedia. Philadelphia, Pennsylvania,2010. New York:ACM,2010:391-400.
[39]Mooney R J, Roy L. Content-based book recommending using learning for text categorization. Proceedings of the 5th ACM Conference on Digital Libraries. San Antonio, USA,2000. New York:ACM, 2010:195-204.
[40]Xiaoyuan Su and Taghi M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, vol.2009, Article ID 421425,19 pages,2009. doi:10.1155/2009/421425
[41]Loannis K,Stathopoulos V, Jose J M. On social networks and collaborative recommendation. Proceedings of the 32nd ACM Special Interest Group on Information Retrieval. Boston, USA,2009. New York:ACM,2009:195-202.
[42]Mahmood T, Ricci F. Towards learning user-adaptive state models in a conversational recommender system. Proceedings of the 15th Workshop on Adaptivity and User Modeling in Interactive Systems. Halle, Germany,2007. Halle:Martin Luther University Press,2007:373-378.
[43]Ricci,F., Cavada, D., Mirzadeh, N., Venturini, A.:Case-based travel recommendations. In:D.R. Fesenmaier, K.Woeber, H.Werthner (eds.) Destination Recommendation Systems:Behavioural Foundations and Applications, pp.67-93. CABI (2006).
[44]Bridge, D., G" oker, M., McGinty, L., Smyth, B.:Case-based recommender systems. The Knowledge Engineering review 20(3),315-320 (2006).
[45]Arazy, O., Kumar, N., Shapira, B.:Improving social recommender systems. IT Professional 11 (4),38-44 (2009).
[46]Ben-Shimon, D., Tsikinovsky, A., Rokach, L., Meisels, A., Shani, G., Naamani, L.:Recommender system from personal social networks. In:K. Wegrzyn-Wolska, P.S. Szczepaniak (eds.) AWIC, Advances in Soft Computing, vol.43, pp.47-55. Springer (2007).
[47]Yoshii K, Goto M, Komatani K, Ogata T, Okuno H G. Hybrid Collaborative and Content-based Music Recommendation Using Probabilistic Model with Latent User Preferences. Proceedings of the 7th International Conference on Music Information Retrieval. Victoria, BC, Canada,2006:296-301.
[48]Basilico J, Hofmann T. Unifying collaborative and content-based filtering. Proceedings of the Twenty-first International Conference on Mechine Learning. Banff, Alberta, Canada,2004. New York: ACM,2004:65-72.
[49]N. Good, J. B. Schafer, J. A. Konstan, A. Borchers, B. M. Sarwar, J. L. Herlocker, and J. Riedl. Combining collaborative filtering with personal agents for better recommendations. In AAAI/IAAI, pages 439-446,1999.
[50]J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and de-velopment in information retrieval, pages 230-237. ACM Press,1999.
[51]Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens:An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 Computer Supported Cooperative Work Conference,1994.
[52]M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Infor-mation Systems,22:143-177,2004.
[53]G. Linden, B. Smith, and J. York, "Amazon.com recommendations:item-to-item collaborative filtering," IEEE Internet Computing, vol.7, no.1, pp.76-80,2003.
[54]Sarwar B M, Karypis G, Konstan J A, Riedl J. Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th ACM International World Wide Web Conference. Hong Kong, China, 2001. New York:ACM,2001:285-295.
[55]Popescul, A., L. H. Ungar, D. M. Pennock, and S. Lawrence. Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In Proc. of the 17th Conf. on Uncertainty in Artificial Intelligence, Seattle, WA,2001.
[56]Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock (2002). "Methods and Metrics for Cold-Start Recommendations". Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002). New York City, New York:ACM. pp.253-260.
[57]M. Balabanovic and Y. Shoham, "Content-based, collaborative recommendation,"Communications of the ACM, vol.40, no.3, pp.66-72,1997.
[58]D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles, "Collaborative filtering by personality diagnosis:a hybrid memory-and model-based approach," in Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI'00), pp.473-480,2000.
[59]Y. Xie, V.V. Phoha, Web user clustering from access log using belief function, in:Proceedings of the First International Conference on Knowledge Capture (K-CAP 2001), ACM Press,2001, pp.202-208.
[60]J. Xiao, Y. Zhang, X. Jia, and T. Li. Measuring Similarity of Interests for Clustering Web-Users. In Proc. of the 12th Australasian Conf. on Database Technologies, pages 107-114, Queensland, Australia, 2001.
[61]Rheingold, H. (2000). The Virtual Community:Homesteading on the Electronic Frontier. London: MIT Press.
[62]M. Girvan and M. E. J. Newman. Community structure in social and biological networks, volume 99, pages 7821-7826,2002.
[63]J. Huang, H. Sun, J. Han, H. Deng, Y. Sun, and Y. Liu. Shrink:a structural clustering algorithm for detecting hierarchical communities in networks. In CIKM, pages 219-228. ACM,2010.
[64]G. Palla, I. Derenyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature,435(7043):814-818,2005.
[65]S. White and P. Smyth. A spectral clustering approach to finding communities in graph. In SDM,2005.
[66]Jon K. Authoritative sources in hyperlinked environment[J]. Journal of the ACM,1999,46(5): 604-632.
[67]Kumar R, Raghavan P, Rajagopalan S, et al. Trawling emerging cyber communities automatically[C]. Proceedings of the 8th International Conference on World Wide Web,1999:1481-1493.
[68]Kumar R, Raghavan P, Rajagopalan S, et al. Extracting large-scale knowledge bases from the web[C]. Proceedings of the 25th International Conference on Very Large Data Bases,1999:639-650.
[69]Flake G W, Lawrence S, Giles C L. Efficient identification of web communities[C]. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2000:150-160.
[70]Miller M, Lynn S, James C. Birds of a feather:Homophily in social networks[J]. Annual Review of Sociology,2001,27(1):415-444.
[71]Golder S, Huberman B. Usage patterns of collaborative tagging system[J]. Journal of Information Science,2006,32(2):198-208.
[72]Li X, Guo L, Zhao Y. Tag-based social interest discovery. Proceedings of the 17th International Conference on World Wide Web,2008:675-684.
[73]Adamic L, Adar E. Friends and neighbors on the web[J]. Social Networks,2003,25(3):211-230.
[74]Matsuo Y, Mori J, Hamasaki M, et al. POLYPHONET:An advanced social network extraction system from the web. Proceedings of the 15th International Conference on World Wide Web,2006:397-406.
[75]Zheleva E, Getoor L. To join or not to join:The illusion of privacy in social networks with mixed public and private user profiles. Proceedings of the 18th International Conference on World Wide Web, 2009:531-540.
[76]Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp:745-752.
[77]H.-P. Kriegel, P. Kroger, and A. Zimek, "Clustering high-dimensional data:A survey on subspace clustering, patternbased clustering, and correlation clustering," TKDD, vol.3, no.1,2009.
[78]M. Pennacchiotti and A.-M. Popescu, "Democrats, republicans and starbucks afficionados:user classification in Twitter," in Proc. ACM KDD 2011, pp.430-438.
[79]Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). Foundations of Machine Learning, The MIT Press.
[80]高文(2013).网络视频服务的关键问题.中国计算机学会通讯,第9卷,第1期.
[81]Cui Bin, Tung A K H, Zhang Ce, Zhao Zhe. Multiple feature fusion for social media applications. Proceedings of ACM Special Interest Group on Management of Data. Indianapolis, Indiana, USA,2010. New York:ACM,2010:435-446.
[82]项亮(2012).推荐系统实践.人民邮电出版社.
[83]Nilesh Bansal, Nick Koudas, BlogScope:A System for Online Analysis of High Volume Text Streams, In Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, Vienna, Austria, Sept 23-28 2007, Demonstration Proposal.
[84]Tomoko Murakami, Koichiro Mori, and Ryohei Orihara. Metrics for evaluating the serendipity of recommendation lists. New Frontiers in Artificial Intelligence,4914:40-46,2008.
[85]Pasquier N, Bastide Y, Taouil R, Lakhal L. Discovering frequent closed itemsets for association rules. Proceeding of the 7th International Conference on Database Theory. Jerusalem, Israel,1999. Berlin: Springer,1999:398-416.
[86]Zaki M J, Parthasarathy S, Ogihara M, and Li Wei. New Algorithms for Fast Discovery of Association Rules. Proceeding of 3rd International Conference on Knowledge Discovery and Data Mining. Newport Beach, USA,1997. Menlo Park:AAAI Press,1997:283-296
[87]Borgelt C. Efficient Implementations of Apriori and Eclat. Proceeding of IEEE International Conference on Data Mining Workshop Frequent Itemset Mining Implementations. Melbourne, USA, 2003. Aachen:CEUR-WS.org,2003:vol.80.
[88]Cheng Zeng, Haiyang Zhou, Bing Yan:Cross-Media Database Retrieval System Based on TOTEM. WISE Workshops 2006:182-193
[89]Zhiyong Peng, Yuan Shi, Boxuan Zhai:Realization of Biological Data Management by Object Deputy Database System.:49-67(2006)
[90]Zhiyong Peng, Qing Li, Ling Feng, Xuhui Li, Junqiang Liu:Using Object Deputy Model to Prepare Data for Data Warehousing. IEEE Trans. Knowl. Data Eng.17(9):1274-1288 (2005)
[91]Zhiyong Peng, Yahiko Kambayashi:Deputy Mechanisms for Object-Oriented Databases. ICDE 1995: 333-340
[92]Duncan J W, Steven H S. Collective dynamics of smallworld networks[J]. Nature,1998,393:440-442.
[93]Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science,1999,286:509-512.
[94]Tantipathananandh C, Berger-Wolf T, Kempe D. A framework for community identification in dynamic social networks, in ope:Parameter-free mining of large timeevolving graphs[C]. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2007:717-726.
[95]Chi Y, Zhu S, Song X, et al. Structural and temporal analysis of the blogosphere through community factorization[C]. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD),2007:163-172.
[96]Lin Y, Chi Y, Zhu S, et al. FacetNet:A framework for analyzing communities and their evolutions in dynamic networks[C]. Proceedings of the 17th International Conference on World Wide Web,2008:685-694.
[98]Hyeongon W, Seungjin O, Jungtae M, et al. A team formation model based on knowledge and collaboration[J]. Expert Systems with Applications,2009,36(5):9121-9134.
[99]Lappas T, Liu K, Terzi E. Finding a team of experts in social networks[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2009:467-476.
[100]A. Anagnostopoulos, L. Becchetti, C. Castillo, A. Gionis, and S. Leonardi. Online team formation in social networks. In Proc. of the International World Wide Web Conference (WWW 2012), pages 839-848, Lyon, France,2012. ACM.