期刊文献+

基于资源类的时间加权协作过滤算法 被引量:1

Time weighted collaborative filtering algorithms based on resources category
下载PDF
导出
摘要 当前e-Learning作为一种重要的教学方式,对其个性化的要求正在日益提高。针对协作过滤推荐是当今应用最为普遍的个性化推荐算法,而数据的稀疏性和算法的可扩展性一直是协作过滤算法所面临的两大问题,提出基于资源类的时间加权协作过滤算法。该算法不仅降低了数据的稀疏性和维度,缩小了目标用户最近邻的查找范围;而且避免了传统推荐算法中推荐值与用户相似度不密切相关的弊端,提高了最近邻的准确度,推荐精度较以往传统算法有明显提高。 Currently e-Learning as an important teaching methods,its individual requirements are increasing. Collaborative filtering is the most common application of personalized recommendation algorithm now, but the sparsity of data and the scalability of algorithm has been a collaborative filtering algorithms faced by the two main issues. Proposed a time weighted collaborative filtering algorithm based on resources category, which not only reduced the data sparsity and dimensionality, and narrowed the area of the nearest neighbor, but also avoided the defects of the traditional recommended method.
作者 章炯 李华
出处 《计算机应用研究》 CSCD 北大核心 2009年第6期2107-2109,共3页 Application Research of Computers
基金 国家"十一五"重大科技攻关项目(2006BAH02A24-6)
关键词 个性化推荐 协同过滤 相似度 聚类 personalized recommendation collaborative filtering similarity clustering
  • 相关文献

参考文献10

二级参考文献68

  • 1周军锋,汤显,郭景峰.一种优化的协同过滤推荐算法[J].计算机研究与发展,2004,41(10):1842-1847. 被引量:103
  • 2秦国,杜小勇.基于用户层次信息的协同推荐算法[J].计算机科学,2004,31(10):138-140. 被引量:15
  • 3王霞,刘琴.协同过滤在推荐系统中的应用研究[J].计算机系统应用,2005,14(4):24-27. 被引量:18
  • 4Brccsc J, Hcchcrman D, Kadic C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI'98). 1998.43~52.
  • 5Goldberg D, Nichols D, Oki BM, Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992,35(12):61~70.
  • 6Resnick P, lacovou N, Suchak M, Bergstrom P, Riedl J. Grouplens: An open architecture for collaborative filtering of netnews. In:Proceedings of the ACM CSCW'94 Conference on Computer-Supported Cooperative Work. 1994. 175~186.
  • 7Shardanand U, Mats P. Social information filtering: Algorithms for automating "Word of Mouth". In: Proceedings of the ACM CHI'95 Conference on Human Factors in Computing Systems. 1995. 210~217.
  • 8Hill W, Stead L, Rosenstein M, Furnas G. Recommending and evaluating choices in a virtual community of use. In: Proceedings of the CHI'95. 1995. 194~201.
  • 9Sarwar B, Karypis G, Konstan J, Riedl J. Item-Based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International World Wide Web Conference. 2001. 285~295.
  • 10Chickering D, Hecherman D. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables.Machine Learning, 1997,29(2/3): 181~212.

共引文献717

同被引文献12

  • 1周军锋,汤显,郭景峰.一种优化的协同过滤推荐算法[J].计算机研究与发展,2004,41(10):1842-1847. 被引量:103
  • 2马文峰,杜小勇.数字资源整合的发展趋势[J].图书情报工作,2007,51(7):66-70. 被引量:40
  • 3SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C] //Proc of the 10th International World Wide Web Conference.New York:ACM Press,2001:285-295.
  • 4HERLOCKER J,KONSTAN J,TERVEEN L,et al.Evaluating collaborative filtering recommender systems[J].ACM Trans on Information Systems,2004,22(1):5-53.
  • 5ESTER M,KRIEFEL H P,SANDER J,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C] //SIMOUDIS E,HAN J W,FAYYAD U M.Proc of the 2nd International Conference on Knowledge Discovery and Data Mining.Portland:AAAI Press,1996:226-231.
  • 6ERTO(O)Z L,STEINBACH M,KUMAR V.Finding clusters of diffe-rent sizes,shapes,and densities in noisy,high dimensional data[C] //Proc of the 2nd SIAM International Conference on Data Mining.2003.
  • 7SARWAR B M,KARYPIS G,KONSTAN J A,et al.Application of dimensionality reduction in recommender system:a case study[C] //Proc of ACM WebKDD 2000 Workshop.2000.
  • 8向坚持,刘相滨,资武成.基于密度的K-Means算法及在客户细分中的应用研究[J].计算机工程与应用,2008,44(35):246-248. 被引量:11
  • 9李春,朱珍民,叶剑,周佳颖.个性化服务研究综述[J].计算机应用研究,2009,26(11):4001-4005. 被引量:34
  • 10周水庚,周傲英,曹晶,胡运发.一种基于密度的快速聚类算法[J].计算机研究与发展,2000,37(11):1287-1292. 被引量:89

引证文献1

二级引证文献12

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部