计算机工程 ›› 2010, Vol. 36 ›› Issue (15): 256-258.doi: 10.3969/j.issn.1000-3428.2010.15.091

• 开发研究与设计技术 • 上一篇    下一篇

基于多代理的个性化推荐学习系统

吴 兵1,2,叶春明1,陈 信2   

  1. (1. 上海理工大学管理学院,上海 2000932. 上海电视大学,上海 200433)

  • 出版日期:2010-08-05 发布日期:2010-08-25
  • 作者简介:吴 兵(1976-),男,讲师、博士研究生,主研方向: 数据挖掘,多代理技术,管理信息系统;叶春明,教授、博士生 导师;陈 信,教授
  • 基金项目:
    上海市教育委员会科研创新基金资助项目(11YZ256); 上海电视大学基金资助项目(JF1004);上海市教育委员会基金资助项目“网上互助交流环节的BDI体系的研究与实现”(06ZZ91)

Personalized Recommendation Learning System Based on Multi-Agent

WU Bing1,2, YE Chun-ming1, CHEN Xin2   

  1. (1. College of Management, University of Shanghai for Science and Technology, Shanghai 200093; 2. Shanghai TV University, Shanghai 200433)
  • Online:2010-08-05 Published:2010-08-25

摘要: 针对现有学习系统存在信息过载、缺乏个性化服务能力、不能提供检索服务的问题,提出基于多代理构建个性化推荐学习系统。该系统利用JADE设计学习者Agent与推荐Agent,采用Lucene设计带有个性化能力的搜索引擎支持推荐,并融合3种推荐方法发挥多Agent间协商与协作的优势。实验结果表明,相比单一推荐方法,该系统具有较好的推荐效果和效率。

关键词: 多代理, 个性化推荐, Lucene搜索引擎, 开放式远程学习

Abstract: Aiming at the problem of overwhelmed information, weak individualized service and the lack of information retrieval ability of current learning system, this paper presents a personalized recommendation learning system based on multi-agent. The system uses Java Agent Development Framework(JADE) to design Learner Agent(LA) and Recommendation Agent(RA), adopts Lucene to build personalized search engine to support recommendation, and combines three kinds of recommended methods to take advantage of cooperation and negotiation among Agents. Experimental results show that this system has better recommendation effect and efficiency compared with single recommendation algorithm.

Key words: multi-Agent, personalized recommendation, Lucene search engine, Open and Distance Learning(ODL)

中图分类号: