Fine-grained news recommendation by fusing matrix factorization, topic analysis and knowledge graph representation

Most news recommendation methods focus on using textual information of news to solve data sparseness problem of collaborative filtering. While if the text is not informative enough, these methods can't work well. A collaborative model combining matrix factorization, topic analysis and knowledge graph representation is proposed by introducing the knowledge from external knowledge base to alleviate the deficiency of the text. The experiment conducted on real life news dataset shows that the joint model outperforms the state-of-the-art method by 14% in Recall@200 metric, and improves the recommendation performance on sparse items by 20%.