摘要
基于Multi-Agent数据挖掘功能,通过对用户初始的注册信息、浏览记录、用户操作行为、相似用户群挖掘,分析得出用户兴趣信息;利用文本特征抽取技术提取相关中文分词,形成有效特征词,采用Multi-Agent技术构建用户兴趣个性化检索引擎模型。引入信息过滤模块和用户兴趣学习模块,根据用户兴趣模型,过滤检索查询结果后再推送给用户,提高信息服务质量,进一步提高了高校图书馆个性化知识服务效率。
Based on the data mining function of Multi-Agent, this paper obtains the user interest information, through analysis of the ini- tial user registration information, browsing history, user behavior and the mining of similar user group, uses the text feature extraction technology to extract relevant Chinese word segmentation to form effective feature words and the Multi-Agent technology to construct per- sonalized search engine model of user interest. This paper introduces information filtering module and user interest learning module. The user interest model can filter the search results, then pushes them to the user, improve the quality of information services, and further im- prove the efficiency of personalized knowledge service in university library.
出处
《南京工业职业技术学院学报》
2015年第4期22-24,共3页
Journal of Nanjing Institute of Industry Technology
