教育信息共享系统中个性化推荐服务研究
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摘要
随着教育信息呈指数增长,用户在拥有丰富的教育信息的同时,也面临着信息过载的困扰,因此实现个性化的教育信息服务对教育信息的共享有着迫切的要求。
本文将基于项目的协同过滤推荐技术应用到教育信息共享领域。由于教育资源大多是难以用关键词描述的媒体资源,因此本文提出采用用户的评价信息作为推荐资源的依据。论文首先阐述教育资源建设的基本理论,以及目前教育资源共享存在的问题,提出了个性化的教育信息服务,并介绍了个性化推荐以及用户建模的关键技术。个性化推荐算法是个性化推荐系统的核心,论文在研究现有个性化推荐算法优缺点的基础上,设计了基于内容推荐和基于项目的协同过滤推荐相结合的教育信息个性化推荐算法,在一定程度上解决了协同过滤推荐的稀疏性问题和可扩展性问题,提高了推荐质量。论文最后设计了教育信息共享系统,及其个性化推荐模块,实践和应用教育信息个性化推荐算法,根据教育资源以及用户学习的特点,设计了用户模型和教育资源模型,实现了教育信息个性化推荐功能,为实现教育信息高效共享和个性化服务提供了新思路。
With the exponential growth of education information, users are encompassed by plenty of education information and faced with the problem of "Information overload" at the same time. Therefore, it is an urgent demand to realize personal education information service for education information sharing.
In this paper, the Item-based Collaborative Filtering Recommendation technology is applied to education information sharing field. Because most of the education information resource is multimedia resource, it can not be described exactly with keywords. So the score which users estimate resource items is used to recommend resources. First, the basic theory of education information construction and the problems in the education resource sharing are stated. Then the key techniques of personal recommendation technology and user modeling are introduced. Personal recommendation algorithm is the core of the personalization recommendation system. Thereby, based on analyzing and researching advantages and disadvantages of the present algorithm, a personal recommendation method of education information with the combination of content-based recommendation and item-based collaborative filtering is designed, which solves the problem of sparsity and scalability to a certain extent. Finally, the design of education information sharing system and the personal recommendation module is brought forward. According to the characteristics of education resources and users' study, the user model and resource model are designed and the function of education information recommendation is implemented, which provides a new clue for realizing education information sharing and personalization recommendation services of education information.
本文将基于项目的协同过滤推荐技术应用到教育信息共享领域。由于教育资源大多是难以用关键词描述的媒体资源,因此本文提出采用用户的评价信息作为推荐资源的依据。论文首先阐述教育资源建设的基本理论,以及目前教育资源共享存在的问题,提出了个性化的教育信息服务,并介绍了个性化推荐以及用户建模的关键技术。个性化推荐算法是个性化推荐系统的核心,论文在研究现有个性化推荐算法优缺点的基础上,设计了基于内容推荐和基于项目的协同过滤推荐相结合的教育信息个性化推荐算法,在一定程度上解决了协同过滤推荐的稀疏性问题和可扩展性问题,提高了推荐质量。论文最后设计了教育信息共享系统,及其个性化推荐模块,实践和应用教育信息个性化推荐算法,根据教育资源以及用户学习的特点,设计了用户模型和教育资源模型,实现了教育信息个性化推荐功能,为实现教育信息高效共享和个性化服务提供了新思路。
With the exponential growth of education information, users are encompassed by plenty of education information and faced with the problem of "Information overload" at the same time. Therefore, it is an urgent demand to realize personal education information service for education information sharing.
In this paper, the Item-based Collaborative Filtering Recommendation technology is applied to education information sharing field. Because most of the education information resource is multimedia resource, it can not be described exactly with keywords. So the score which users estimate resource items is used to recommend resources. First, the basic theory of education information construction and the problems in the education resource sharing are stated. Then the key techniques of personal recommendation technology and user modeling are introduced. Personal recommendation algorithm is the core of the personalization recommendation system. Thereby, based on analyzing and researching advantages and disadvantages of the present algorithm, a personal recommendation method of education information with the combination of content-based recommendation and item-based collaborative filtering is designed, which solves the problem of sparsity and scalability to a certain extent. Finally, the design of education information sharing system and the personal recommendation module is brought forward. According to the characteristics of education resources and users' study, the user model and resource model are designed and the function of education information recommendation is implemented, which provides a new clue for realizing education information sharing and personalization recommendation services of education information.
引文
[1]Gediminas Adomavicius,Alexander Tuzhilin.Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions[J].IEEE TRANSANTIONS ON KNOWLEDGE AND ENGINEEERING,2005,17(6):734-749.
[2]Paul Resnick,Hal R.Varian,Recommender System[J].communications of the ACM,1997,40(3):56-58.
[3]Bing Xu,Mingmin Zhang,Zhigeng Pan,etc.Content-Based Recommendation in E-Commerce[J].Computational Science and Its Application,2005,3481:946-955.
[4]Joseph A.Konstan,Bradley N.Miller,David Maltz,etc.Applying Collaborative Filtering to Usenet News[J].Communications of the ACM,1997,40(3):77-87.
[5]The Art,Science and Business of Recommendation Engines[EB/OL].http://www.readwriteweb.com/archives/recommendation_engines.php.
[6]GroppLens[EB/OL].http://grouplens.org/.
[7]Pandora[EB/OL].http://www.pandora.com/mgp.shtml.
[8]The Del.icio.us Lesson[EB/OL].http://bokardo.com/archives/the-delicious-lesson/.
[9]Adomavicius,G.,Tuzhilin,A..User profiling in personalization applications through rule discovery and validation[C].Proceedings of the 5th International Conference on Data Mining and Knowledge Discovery.1999.377-381.
[10]Michael J.Pazzani,Daniel Billsus.Content-Based Recommendation System[J].The Adaptive Web,4321,325-341.
[11]Herlocker,J.Understanding and Imporving Automated Collaborative Filtering Systems[D].Ph.D Thesis,Science Computer Dept.,University of Minnesota,2000.
[12]Sarwar B,Karypis G,Konstan J,Riedl J.Item-Based collaborative filtering recommendation algorithms[C].Proceedings of the 10th International World Wide Web Conference.2001.285-295.
[13]Robert Armstrong,Dayne Freitag,Thorsten Joachims,etc.WebWatcher:A Learning Apprentice for the World Wide Web[J].In Working Notes of the AAAI Spring Symposium:Information Gathering form Heterogeneous,Distributed Environments,1995,pp.6-12
[14]曾春,邢春晓,周立柱.个性化服务技术综述[J].2002,13(10):1952-1961.
[15]Jonthan L.Herlocker,Joseph A.Konstan,Loren G.Terveen,etc.Evaluating Collaborative Filtering Recommender Systems[J].ACM Transactions on Information System,2004,22(1):5-53.
[16]余力,刘鲁,罗掌华.我国电子商务推荐策略的比较分析[J].系统工程理论与实践,2004,(8):96-101.
[17]赵亮,胡乃静,张守志.个性化推荐算法设计[J].计算机研究与发展,2002,39(8):986-991.
[18]孙小华.协同过滤系统的稀疏性与冷启动问题研究[D].浙江大学博士学位论文,2005。
[19]Jun Wang,Arjen P.de Vries,Marcel J.T.Reinders.Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion[J].Proc.29~(th)ACM SIGIR Conference on Information Retrieval,2006,pp.501-508.
[20]刘建国。基于教学资源库系统的个性化推荐模式的设计与实现[C].第16届全国计算机新科技与教育研讨会.PP549-552.
[21]丁琳。数据挖掘在远程教育个性化服务中的应用[J).电化教育研究,2002,9.
[22]王琳.结合内容预测和Item-based协同过滤的推荐系统研究[D].山东大学硕士学位论文,2005.
[23]推荐系统:Recommender Systems简介[EB/OL].http://www.guwendong.cn/catalog.asp?cate=1 &page=3.
[24]个性化站点:MovieLens.org[EB/OL].http://www.guwendong.cn/post/2007/site_movielens_org.html.
[25]冯翱,刘斌,王普等.Open Bookmark——基于Agent的信息过滤系统[J].清华大学学报,2001,41(3):85-88.
[26]教育资源建设技术规范CELTS-31[S]
[27]推荐引擎和个性化推荐[EB/OL].http://subject.csdn.net/tuijian/
[28]余力.电子商务个性化推荐若干问题研究[D].北京航空航天大学博士论文,2004.
[29]推荐引擎:从搜索到发现[EB/OL].http://blog.csdn.net/java060515/archive/2007/04/18/1568658.aspx
[30]Robin van Meteren,Maarten van Someren.Using Content-Based Filtering for Recommendation[J].Proceedings of the First International Conference on Knowledge and Data Mining,1995,pp.210-215.
[31]朱凌云.教育资源建设标准化研究[D].北京师范大学硕士学位论文,2003.
[32]元小涛.网络教学资源库建设研究[D].曲阜师范大学硕士学位论文,2004.
[33]刘建国.教学资源个性化服务模型及实现技术研究[D]。西南师范大学硕士学位论文,2004.
[34]徐小琳,阙喜戎,程时端.信息过滤技术和个性化信息服务IJ).计算机工程与应用,2003..39(9):182-184。
[35]杨炎,孙铁利,邱春艳。个性化推荐技术的研究rJ).信息工程大学学报,2005,6(2):84-87.
[36]韩晓莉,李秉智.个性化Web推荐服务研究LJ).计算机科学,2006,33(2):135-141.
[37]信息过滤技术探讨[EB/OL].http://www.eenet.org.cn/article.asp?articleid=17326.
[38]Breese J.S,Hecherman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C].Proceedings of the 14th conference on Uncertainty inArtifieial Intelligence.1998.43-52.
[39]余侠,朱林.根据用户反馈建立和更新数字图书馆用户兴趣模型[J].情报杂志,2004,(11):21-22.
[40]吴丽花,刘鲁.个性化推荐系统用户建模技术综述IJ).2006,25(1):55-62.
[41]韩鹏.个性化E-elearning环境中智能用户建模技术研究[D].上海交大大学博士学位论文,2004。
[42]推荐系统:协同过滤之Item-Based CollaboratiVe Filtedng[EB/OL].http://www.guwendong.cn/catalog.asp?cate=1&page=2.
[43]宋丽哲,牛振东,宋瀚涛等.数字图书馆个性化服务用户模型研究[J].2005,25(1):58-62.
[44]汤显.基于类别相似性和分类方法的个性化推荐方法研究[D].燕山大学硕士学位论文,2005.
[45]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,(09):1-8。
[46]陈静.基于关联规则的推荐系统研究[D].西安电子科技大学硕士论文,2003.
[47]江宝林.个性化推荐系统研究[D].复旦大学硕士学位论文,2004.
[48]个性化:定制你的网络服务[EB/OL].http://tech.tom.com/Archive/1796/1800/2003/6/30-48922.html.
[49]程明.基于文本分类与用户兴趣的个性化搜索与推荐的研究与实现[D].中山大学硕士学位论文,2006.
[2]Paul Resnick,Hal R.Varian,Recommender System[J].communications of the ACM,1997,40(3):56-58.
[3]Bing Xu,Mingmin Zhang,Zhigeng Pan,etc.Content-Based Recommendation in E-Commerce[J].Computational Science and Its Application,2005,3481:946-955.
[4]Joseph A.Konstan,Bradley N.Miller,David Maltz,etc.Applying Collaborative Filtering to Usenet News[J].Communications of the ACM,1997,40(3):77-87.
[5]The Art,Science and Business of Recommendation Engines[EB/OL].http://www.readwriteweb.com/archives/recommendation_engines.php.
[6]GroppLens[EB/OL].http://grouplens.org/.
[7]Pandora[EB/OL].http://www.pandora.com/mgp.shtml.
[8]The Del.icio.us Lesson[EB/OL].http://bokardo.com/archives/the-delicious-lesson/.
[9]Adomavicius,G.,Tuzhilin,A..User profiling in personalization applications through rule discovery and validation[C].Proceedings of the 5th International Conference on Data Mining and Knowledge Discovery.1999.377-381.
[10]Michael J.Pazzani,Daniel Billsus.Content-Based Recommendation System[J].The Adaptive Web,4321,325-341.
[11]Herlocker,J.Understanding and Imporving Automated Collaborative Filtering Systems[D].Ph.D Thesis,Science Computer Dept.,University of Minnesota,2000.
[12]Sarwar B,Karypis G,Konstan J,Riedl J.Item-Based collaborative filtering recommendation algorithms[C].Proceedings of the 10th International World Wide Web Conference.2001.285-295.
[13]Robert Armstrong,Dayne Freitag,Thorsten Joachims,etc.WebWatcher:A Learning Apprentice for the World Wide Web[J].In Working Notes of the AAAI Spring Symposium:Information Gathering form Heterogeneous,Distributed Environments,1995,pp.6-12
[14]曾春,邢春晓,周立柱.个性化服务技术综述[J].2002,13(10):1952-1961.
[15]Jonthan L.Herlocker,Joseph A.Konstan,Loren G.Terveen,etc.Evaluating Collaborative Filtering Recommender Systems[J].ACM Transactions on Information System,2004,22(1):5-53.
[16]余力,刘鲁,罗掌华.我国电子商务推荐策略的比较分析[J].系统工程理论与实践,2004,(8):96-101.
[17]赵亮,胡乃静,张守志.个性化推荐算法设计[J].计算机研究与发展,2002,39(8):986-991.
[18]孙小华.协同过滤系统的稀疏性与冷启动问题研究[D].浙江大学博士学位论文,2005。
[19]Jun Wang,Arjen P.de Vries,Marcel J.T.Reinders.Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion[J].Proc.29~(th)ACM SIGIR Conference on Information Retrieval,2006,pp.501-508.
[20]刘建国。基于教学资源库系统的个性化推荐模式的设计与实现[C].第16届全国计算机新科技与教育研讨会.PP549-552.
[21]丁琳。数据挖掘在远程教育个性化服务中的应用[J).电化教育研究,2002,9.
[22]王琳.结合内容预测和Item-based协同过滤的推荐系统研究[D].山东大学硕士学位论文,2005.
[23]推荐系统:Recommender Systems简介[EB/OL].http://www.guwendong.cn/catalog.asp?cate=1 &page=3.
[24]个性化站点:MovieLens.org[EB/OL].http://www.guwendong.cn/post/2007/site_movielens_org.html.
[25]冯翱,刘斌,王普等.Open Bookmark——基于Agent的信息过滤系统[J].清华大学学报,2001,41(3):85-88.
[26]教育资源建设技术规范CELTS-31[S]
[27]推荐引擎和个性化推荐[EB/OL].http://subject.csdn.net/tuijian/
[28]余力.电子商务个性化推荐若干问题研究[D].北京航空航天大学博士论文,2004.
[29]推荐引擎:从搜索到发现[EB/OL].http://blog.csdn.net/java060515/archive/2007/04/18/1568658.aspx
[30]Robin van Meteren,Maarten van Someren.Using Content-Based Filtering for Recommendation[J].Proceedings of the First International Conference on Knowledge and Data Mining,1995,pp.210-215.
[31]朱凌云.教育资源建设标准化研究[D].北京师范大学硕士学位论文,2003.
[32]元小涛.网络教学资源库建设研究[D].曲阜师范大学硕士学位论文,2004.
[33]刘建国.教学资源个性化服务模型及实现技术研究[D]。西南师范大学硕士学位论文,2004.
[34]徐小琳,阙喜戎,程时端.信息过滤技术和个性化信息服务IJ).计算机工程与应用,2003..39(9):182-184。
[35]杨炎,孙铁利,邱春艳。个性化推荐技术的研究rJ).信息工程大学学报,2005,6(2):84-87.
[36]韩晓莉,李秉智.个性化Web推荐服务研究LJ).计算机科学,2006,33(2):135-141.
[37]信息过滤技术探讨[EB/OL].http://www.eenet.org.cn/article.asp?articleid=17326.
[38]Breese J.S,Hecherman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C].Proceedings of the 14th conference on Uncertainty inArtifieial Intelligence.1998.43-52.
[39]余侠,朱林.根据用户反馈建立和更新数字图书馆用户兴趣模型[J].情报杂志,2004,(11):21-22.
[40]吴丽花,刘鲁.个性化推荐系统用户建模技术综述IJ).2006,25(1):55-62.
[41]韩鹏.个性化E-elearning环境中智能用户建模技术研究[D].上海交大大学博士学位论文,2004。
[42]推荐系统:协同过滤之Item-Based CollaboratiVe Filtedng[EB/OL].http://www.guwendong.cn/catalog.asp?cate=1&page=2.
[43]宋丽哲,牛振东,宋瀚涛等.数字图书馆个性化服务用户模型研究[J].2005,25(1):58-62.
[44]汤显.基于类别相似性和分类方法的个性化推荐方法研究[D].燕山大学硕士学位论文,2005.
[45]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,(09):1-8。
[46]陈静.基于关联规则的推荐系统研究[D].西安电子科技大学硕士论文,2003.
[47]江宝林.个性化推荐系统研究[D].复旦大学硕士学位论文,2004.
[48]个性化:定制你的网络服务[EB/OL].http://tech.tom.com/Archive/1796/1800/2003/6/30-48922.html.
[49]程明.基于文本分类与用户兴趣的个性化搜索与推荐的研究与实现[D].中山大学硕士学位论文,2006.