摘要
为了提高情报分发的效率,解决雷达组网上信息过载的问题,提出了一种利用个性化推荐技术过滤情报用户感兴趣的情报信息的技术。根据情报用户兴趣多样性的特点和雷达情报的格式化特征,对情报用户兴趣的类别进行划分,并设计出基于层次向量空间模型;在此基础上,利用用户的历史情报信息和定制信息,运用TF-IDF算法挖掘用户兴趣,建立用户兴趣模型,通过实时情报与用户兴趣模型的匹配,将用户感兴趣的情报分发给用户。仿真实验结果表明,该算法能够较好地实现雷达情报的按需分发。
To improve the efficiency of intelligence distribution,and solve the problem of the information overload on the radar intelligence network,the technology of filtering the users＇ interested intelligence based on the personalized recommending techniques is proposed.Because of the diversity of users＇ interests and the special format of radar intelligence,the users＇ interests are divided into several sorts,and then the hierarchical vector space model is designed.Based on these,historical information and registered data are utilized for TF-IDF classifier to mine the users＇ interests,which is presented by the hierarchical vector space model.With the discovered user profile,the most matched intelligence is recommended to the users.And the results show that this algorithm can realize radar data distribution for the personalized demands.
出处
《计算机工程与设计》
CSCD
北大核心
2012年第5期1822-1826,共5页
Computer Engineering and Design
关键词
情报按需分发
个性化推荐
层次向量空间模型
兴趣模型
词频-逆文档频率
radar data distribution on demands
personalized recommendation
hierarchical vector space model
user profiling
TF-IDF
