摘要
借鉴Web2.0、社交网络、复杂网络、本体论和云计算等理论,设计了基于用户兴趣图谱的个性化推荐系统结构,阐明了基于用户兴趣图谱的推荐原理,提出了用户兴趣图谱生成与集成方法,以及用户兴趣图谱的动态演化与反馈机制,提高了推荐系统的推荐质量和精度。
Web2.0, social network, complex network, ontology theory and cloud computing were used as sources of reference to design personalized recommendation system structure .The theory of recommendation based on user interest graph was explained .The methods of user interest graph generation and integration were put forward ;dynamic evolution and feedback mechanism were discussed .The recommendation quality and accuracy of the recommendation system were improved .
出处
《武汉理工大学学报（信息与管理工程版）》
CAS
2014年第3期341-344,387,共5页
Journal of Wuhan University of Technology：Information & Management Engineering
基金
国家科技支撑计划基金资助项目(2013BAH13F01
2012BAH93F04)
中央高校基本业务专项资金资助项目(2012-IB-060)
关键词
兴趣图谱
个性化推荐
云计算
本体
interest graph
personalized recommendation
cloud computing
ontology
