【摘要】 为实现个性化服务,理解用户兴趣就成了提供服务的关键任务,因此,提出了隐性采集用户浏览内容、用户浏览时间和用户操作时间的信息方法,通过对网络爬虫程序抓取的网页进行内容清洗提取出主要内容之后,利用VSM建立文档模型,并采用SVM分类方法建立推荐库。基于从客户端采集的用户兴趣信息建模,以及根据该模型和推荐库的相似度,给用户推荐信息。此外,给出了基于该模型的推荐原型系统的实现,使用查准率来评价该系统。试验结果表明,系统较好地实现了基于用户兴趣来推荐阅读的信息。更多还原

【Abstract】 In order to implement personalized services, understanding user interest becomes the critical mission for providing services. So an information method for hidden collection of browsing content, browsing time span and operation time span is proposed. The key elements are extracted through cleaning the content of web page which crawled by web crawling program. Then the document model is built via VSM and the recommended library is built by using class method of SVM. The model is built to recommend information to user based on user interest information collected from client and the similarity between this model and recommended library. In addition, implementing of the recommendation system is introduced. The precision ratio is used to evaluate this system, and it’s experimental results show that our system has good at recommending information to users according to their interests.更多还原