A Tag-Based Personalized News Recommendation Method
Shen Yan, Pengqiang Ai, Yingyuan Xiao, Wenguang Zheng, Wenxin Zhu · 2018
Although the news tags show the topic information that is closely related to the news content, the existing personalized news recommendation methods usually ignore the value of tags. In this paper, the probability relation graph among tags is first established by mining the potential correlation among different tags. On this basis, we leverage the term frequency-inverse document frequency (TF-IDF)to find the way of calculating the weights of tags and propose the method of calculating the correlation degree between tags by means of the conditional probability. Finally, a personalized news recommendation method based on the probability relation graph among tags is proposed by leveraging the weights of tags and the correlation degree between tags, and the validity of the proposed method is verified by the experiments on the real data set.