Personalized Recommendation System Based on Multi_Agent and Rough Set
Recommendation technology is an important technology to solve problems of information overwhelmed. However, recommendation system has many shortages such as lack of ability to discover knowledge hidden in data of users online behaviours, poor personalized service, weak recommendation efficiency and effectiveness. In this paper, we put forward a personalized recommendation system based on multiagent. We use JADE to build a learner agent (LA) and three types of recommendation agent (RA). RA takes advantage of rough set theory to discover pattern of users interest based on users online behaviors. We adopt Lucene to analyse materials, which are accessed by users, and extract information from them in order to build decision table and support RA to carry on knowledge discovery work. In the meantime, we use Lucene to build personalized retrieval function to support recommendation. The system integrates advantages of various recommended methods combining with the advantages of knowledge discovery work. Experiments based on real data show that this mechanism can recommend proper learning resources to users.
Agent Rough Set Personalized Recommendation JADE Lucene
Wu Bing Wu Fei Ye Chunming
College of Management, University of Shanghai for Science and Technology,Deans office, Shanghai TV Doctor, Shanghai Science & Technology Development and Exchange Center Shanghai, P.R.China Professor, College of Management, University of Shanghai for Science and Technology Shanghai, P.R.Ch
国际会议
上海
英文
303-307
2010-06-22（万方平台首次上网日期，不代表论文的发表时间）