语义分析与TF-IDF方法相结合的新闻推荐技术

News Recommendation Technology Combining Semantic Analysis with TF-IDF Method

在新闻项目的推荐系统中,通常使用TF-IDF权重技术结合余弦相似性度量方法,然而这种技术没有考虑到文字本身的实际语义,因此,提出了基于内容和语义分析相结合的一种新方法。此方法将同义词集合的逆文档频率及语义相似性相结合,采用WordNet同义词集合做相似性计算。构建用户配置文件进行实验测试,验证了该方法的有效性。实验结果表明,提出的语义方法性能优于TF-IDF方法。

Currently in the news item recommendation system, usually using TF-IDF weighting technology combined with the cosine similarity measure, however, this technique does not take into account the actual semantics of the text itself, therefore, the paper propsed a new method based on the combination of contents and their semantic similarities. This method is a collection of synonyms and inverse document frequency combining semantic similarity using WordNet synset do similar calculations. Building user profiles for laboratory tests to verify the effectiveness of the method. Experimental results show that the proposed method outperforms the TF-IDF method.