期刊文献+

一种分布式智能推荐系统的设计与实现 被引量:1

Design and Implemention of a Distributed Intelligent Suggestion System
下载PDF
导出
摘要 E-Learning作为一种全新的网络教育模式,为在线学员提供越来越多学习资源的同时,其结构也变得更加复杂,在线学员经常会迷失在大量的信息空间中,无法顺利找到自己所需的学习资源。引入移动Agent技术,提出一种面向E-Learn-ing的集成群Agent与Web服务的分布式智能推荐系统(Multi Agent System&Web Services Intelligent Recommendation System,MASWSIRS),其能有效地帮助学员找到所需的信息。构造了MASWSIRS的体系结构,说明了系统的工作流程;详细阐述了MASWSIRS的各主要功能模块的实现算法,包括系统整体实现算法、系统聚簇算法及推荐算法。通过对系统的应用与性能监测来看,系统实现切实可行且运行性能良好。 As a novel web - based education model, the structure of E - Learning is becoming more and more complex while providing more and more learning resource for learners. On - line learners are often lost in the great information space and can not find learning resource needed by them all right. So the e - learning suggestion system comes up. It can assist the learners to search learning resource needed effectively. This paper puts forward a kind of travel distributed intelligent recommendation system based on mobile agent and web services (MASWSIRS) and constructs the architecture of the system. Meanwhile the paper narrates the workflow of the system. Finally the paper particularly explains the implementing algorithms of main functions of the system including the global implementing algorithm, clustering algorithm and suggestion algorithm. The system is evaluated by real application and the experiments show that it is feasible and effective.
作者 陶剑文
出处 《计算机仿真》 CSCD 2007年第7期296-300,共5页 Computer Simulation
基金 浙江省教育厅科研项目资助(20040120)
关键词 移动代理 个性化 推荐系统 网络使用挖掘 服务合成 Mobile agent Personalization Suggestion system Web usage mining( WUM ) Services synthesizing
  • 引文网络
  • 相关文献

参考文献6

  • 1B Mobasher,B Cooley & J Srivastava.Automatic Personalization Based on Web Usage Mining[J].Communications of the ACM,2000,43(8):142-151.
  • 2M Perkowitz & O Etzioni.Adaptive Web Sites:Conceptual Cluster Mining[C].Proceedings of the International Joint Conference on Artificial Intelligence,1999.264-269.
  • 3F Silvestri,R Baraglia,P Palmerini & M Serranò.On-line Generation of Suggestions for Web Users[C].Proceedings of the IEEE International Conference on Information Technology:Coding and Computing,ITCC 2004.
  • 4张云勇 刘锦德.移动agent技术[M].北京:清华大学出版社,2003..
  • 5Peter Dolog.Personalization in Distributed e-Learning Environments[J].Communications of ACM.2004,(11):170-178.
  • 6胡迎松,韩苹,陈中新.一个基于Agent的个性化推荐系统[J].计算机应用研究,2006,23(4):78-80. 被引量:19

二级参考文献5

  • 1MR Dimitre Dimitrov,DR James Warren.More Efficient Web Searching by Modeling Users' Long-term Goals[C].Proceedings of the IAT'99,1999.342-346.
  • 2Mobasher B,Dai H.Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data[C].Proceedings of the ITWP'01,2001.
  • 3Y Y Yao,et al.Web Intelligence (WI) Research Challenges and Trends in the New Information Age[C].Web Intelligence:Research and Development,2001.8-13.
  • 4Mobasher B,Dai H.Integrating Web Usage and Content Mining for More Effective Personalization[C].Proceedings of the ECWeb'00,2000.
  • 5Myungeun Lin,Juntae Kim.An Adaptive Recommendation System with a Coordinator Agent[C].Proceedings of WI'01,2001.

共引文献149

同被引文献11

  • 1刘平峰,聂规划,陈冬林.基于知识的电子商务智能推荐系统平台设计[J].计算机工程与应用,2007,43(19):199-201. 被引量:19
  • 2Wang J, de Vries A P, Reinders M J T. Unifying user-based and item-based collaborative filtering approaches by similarityfusion [ C ]//Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. New York : ACM ,2006:501-508.
  • 3Deshpande M, Karypis G. Item-based top-n recommendation algorithms [ J ]. ACM Transactions on Information Systems, 2004,22( 1 ) : 143-177.
  • 4Linden G,Smith B, York J. Amazon. com recommendations: item-to-item collaborative filtering[ J ]. IEEE Internet Computing,2003,7( 1 ) :76-80.
  • 5Liu Q. Research on some key technologies of Chinese-English machine-in translation [ D ]. Beijing: Peking University,2004.
  • 6Sarwar B, Karypis G, Konstan J, et al. Itembased collaborative filtering recommendation algorithms [ C ]//Proceedings of the 10th international worldwide web conference. [ s. l. ] : [ s. n. ] ,2001:285-295.
  • 7黄创光,印鉴,汪静,刘玉葆,王甲海.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,33(8):1369-1377. 被引量:217
  • 8李莉,廖剑伟,欧灵.云计算初探[J].计算机应用研究,2010,27(12):4419-4422. 被引量:55
  • 9熊忠阳,刘芹,张玉芳,李文田.基于项目分类的协同过滤改进算法[J].计算机应用研究,2012,29(2):493-496. 被引量:39
  • 10陈如明.大数据时代的挑战、价值与应对策略[J].移动通信,2012(17):14-15. 被引量:168

引证文献1

二级引证文献1

;
使用帮助 返回顶部