基于典型反馈的个性化文本信息过滤

Personalized text content filtering based on typical feedbacks

采用了内容过滤和协同过滤相结合的混合过滤模式,利用高级语义知识进行Web文档的主题归类,并引入了典型推荐的思想把用户进行归类。文档和用户的兴趣文件根据文档(兴趣文档)在分类主题上的概率分布来表示,通过典型反馈信息进行用户兴趣文件的更新。最后经过实验测试,精确率和召回率都有明显的提高。

This paper presented an approach to perform text content filtering based on content-based and collaborative filtering, using the Probability Model. Introducing the idea of Stereotypic inference, it classified the users into different types and built the model for each type. Moreover, the refreshing of the profiles was based on the feedbacks of the model, and the building of the new profiles was based on the typical profiles. In this way, the precision and the recall were improved significantly.