期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

一种基于协同过滤技术的自适应推荐系统 被引量：3

A Kind of Adaptive Recommend System Based on Collaborative Filtering Technology
下载PDF
导出
摘要 在电子商务时代,提供更加人性化、个性化的服务是一个电子商务网站生存的关键。提出一种自适应推荐框架,通过组合各种原子推荐算法来提高推荐的准确性;提出采用平均法和分组调整法两种机制来处理各原子算法的结合问题。实验表明,这两种方法都取得了良好的推荐效果。该框架能够有效避免单一推荐算法的缺陷,更好地适应电子商务推荐应用。 In e-commerce times, it is important for an e-commerce website whether it can provide better personality and individuality services for customers. An adaptive recommend framework was presented and accuracy of recommendation was improved through combining some basic recommendation algorithms. Average method and grouping adjustment method were given to solve combination problems of these basic algorithms. The experiment shows that the two methods have well recommend results. The framework can avoid defects of single algorithm, and it is more suitable for recommend application of e- commerce.
作者 刘洋
机构地区 辽宁石油化工大学计算机与通信工程学院
出处 《辽宁石油化工大学学报》 CAS 2007年第3期75-78,共4页 Journal of Liaoning Petrochemical University
关键词 推荐系统 协同过滤 自适应 Recommend system Collaborative filtering Adaptive
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献8

  • 1陈明明,赵连昌.因子定理在网络可靠性分析中的应用[J].石油化工高等学校学报,2002,15(1):70-73. 被引量：1
  • 2Resnick P,Iacovou N,Suchak M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C].In transcending boundaries,proceedings of the computer supposed cooperative workconference.North Carolina,US:UNC Press,1994:175-186.
  • 3Goldberg D,Nichols D,Oki B M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of the ACM,1992,35(12):61-70.
  • 4Sarwar B,Karypis G,Konstan J,et al.Item-based collaborative filtering recommendation algorithms[C].In proceedings of the 10th international world wide web conference.Hong Kong,China:ACM Press,2001:285-295.
  • 5Marko Balabanovic,Yoav Shoham.Fab:Content-based,collaborative recommendation[J].Communications of the ACM,1997,40(3):66-72.
  • 6邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628. 被引量：555
  • 7Ghani R,Fano A.Building recommender systems using a knowledge base of product semantics[C].In proceedings of the workshop on recommendation and personalization in e-commerce.Malaga,Spain:IEEE press,2002:120-135.
  • 8Manolis Vozalis,Konstantinos Margaritis.Unison-CF:A multiple-component,adaptive collaborative filtering system[C].Germany:Springer,2004:255-264.

二级参考文献15

  • 1Brccsc 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.
  • 2Goldberg D, Nichols D, Oki BM, Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992,35(12):61～70.
  • 3Resnick 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.
  • 4Shardanand 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.
  • 5Hill 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.
  • 6Sarwar 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.
  • 7Chickering D, Hecherman D. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables.Machine Learning, 1997,29(2/3): 181～212.
  • 8Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1977,B39:1～38.
  • 9Thiesson B, Meek C, Chickering D, Heckerman D. Learning mixture of DAG models. Technical Report, MSR-TR-97-30, Redmond:Microsoft Research, 1997.
  • 10Sarwar B, Karypis G, Konstan J, Riedl J. Analysis of recommendation algorithms for E-commerce. In: ACM Conference on Electronic Commerce. 2000. 158～167.

共引文献554

同被引文献19

  • 1陈旭辉,王益民,蒋红,夏湘玲.基于XML/RDF语义Web描述的个性化服务系统[J].兰州理工大学学报,2005,31(6):82-84. 被引量：1
  • 2袁燚,张璟,李军怀.基于网页关键词的个性化Web推荐算法[J].西安理工大学学报,2007,23(1):59-61. 被引量：1
  • 3Pawlak Z. Rough set theory and its applications to data analysis[J].Cybernetics and system, 1998, 29(27).. 661-688.
  • 4Hu X H, Cercone N. Learning in relational database:a rough set approach[J]. Computational intelligence, 1995, 11(2):323-337.
  • 5Hu Keyun, Lu Yuchang, Shi Chunyi. Feature ranking in rough sets[J]. AI communications, 2003,16: 41-50.
  • 6BASU C,HIRSH H,COHEN W.Recommendation as classification:using social and content-based information in recommendation[C] //Proceedings of the 15th National Conference on Artificial Intelligence.Madison,WI:[s.n.] ,1998:714-720.
  • 7GOLDBERG D,NICHOLS D,OKI B M,et al.Using collaborative filtering to weave an information tapestry[J].Communication of the ACM,1992,35(12):61-70.
  • 8Kohavi R. Applications of data mining to electronic commerce [J]. Data mining and knowledge discovery, 2001, 5(1) :5-10.
  • 9Scllafer J B, Konstan J A, Riedl J. E-commerce recommendation applications [J]. Data mining and knowledge discovery, 2001,5(1):115-153.
  • 10Steve Cayzer, Uwe Aickelin. A Recommender system based on the immune network// Proceedings CEC2002[C], Honolulu: IEEE press, 2002: 807 - 813.

引证文献3

二级引证文献2

辽宁石油化工大学学报
2007年 第3期

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