Fine-Grained Deep Knowledge-Aware Network for News Recommendation with Self-Attention

Abstract

On-line news reading has become the most popular way for user to obtain real-time information. With the millions of news, it is a key challenge to help user find the articles that are interesting to read. Although great achievements have been made, there is little work to focus on combing news language with external knowledge graphs and expanding news text from a word-level. Taking this issue into consideration, we introduce a novel self-attention based mechanism in news recommendation. The key component of our model is multiple self-attention modules: the word-level attention, which takes tags of news, entities in external knowledge graph and entities' contexts as the input to calculate the semantic-level and knowledge-level representation of the news; the item-level attention module, which used to fuse the two-level representation into the same low-dimension and get a overall embedding of user history behavior sequence. Specially, in order to deal with the diversity of user preferences, we use another self-attention module dynamically aggregate user click history and select candidate news. And finally, a multi-head attention module is used to connect history and candidate news and then calculate the click-through-rate(CTR) via a fully connected layer. Through amount of experiments on a real-world online news website, we demonstrate that our model outperforms better results than previous start-of-art recommendation models.