  Self-attention based models are widely used in news recommendation tasks.
However, previous Attention architecture does not constrain repeated
information in the user's historical behavior, which limits the power of hidden
representation and leads to some problems such as information redundancy and
filter bubbles. To solve this problem, we propose a personalized news
recommendation model called DCAN.It captures multi-grained user-news matching
signals through news encoders and user encoders. We keep updating a coverage
vector to track the history of news attention and augment the vector in 4 types
of ways. Then we fed the augmented Coverage vector into the Multi-headed
Self-attention model to help adjust the future attention and added the Coverage
regulation to the loss function(CRL), which enabled the recommendation system
to consider more about differentiated information. Extensive experiments on
Microsoft News Recommendation Dataset (MIND) show that our model significantly
improve the diversity of news recommendations with minimal sacrifice in
accuracy.
