基于多代理的个性化推荐学习系统

Personalized Recommendation Learning System Based on Multi-Agent

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。

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.