  Online social media platforms offer access to a vast amount of information,
but sifting through the abundance of news can be overwhelming and tiring for
readers. personalised recommendation algorithms can help users find information
that interests them. However, most existing models rely solely on observations
of user behaviour, such as viewing history, ignoring the connections between
the news and a user's prior knowledge. This can result in a lack of diverse
recommendations for individuals. In this paper, we propose a novel method to
address the complex problem of news recommendation. Our approach is based on
the idea of dual observation, which involves using a deep neural network with
observation mechanisms to identify the main focus of a news article as well as
the focus of the user on the article. This is achieved by taking into account
the user's belief network, which reflects their personal interests and biases.
By considering both the content of the news and the user's perspective, our
approach is able to provide more personalised and accurate recommendations. We
evaluate the performance of our model on real-world datasets and show that our
proposed method outperforms several popular baselines.
