基于语义Web的网页推荐模型

如何使用语义 Web技术构建网页推荐模型是一个有着广泛应用前景的研究课题。语义 Web下的网页用本体标注 ,该模型自动采集被标注网页上的语义信息 ,利用网页中的标注信息和相应的本体概念对网页进行分类 ,将分类结果存放在数据库中。同时 ,在这个模型下 ,用户的兴趣存放在DAML (DARPA agent markup language)格式的文件中。通过用户兴趣和网页类别的匹配 ,就能够给用户推荐需要的网页 ,在推荐网页时 ,该模型还采用支持向量机用于分类用户。实验结果显示了该模型比传统的网页推荐模型准确率更高

Web page recommendation model for the semantic web

Web pages are annotated by ontology in the semantic web. This paper describes a web page recommendation model for the semantic web which collects the annotation information in the web pages, uses the annotations to classify the web pages and stores the web page's uniform resource locator and its type in a database. The users' interests are stored in DARPA agent markup language and ontology inference language files. A support vector machine is also used to classify the users. The model matches the web pages' types to the users' interests to decide which web pages to recommend. Test results show that the method gives better performance than traditional web page recommendation methods in the traditional web.