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.

翻译：在线社交媒体平台提供了海量信息的访问渠道，但面对铺天盖地的新闻内容，读者往往感到不堪重负且疲惫不堪。个性化推荐算法能够帮助用户找到其感兴趣的信息。然而，现有的大多数模型仅依赖用户行为观察（如浏览历史），忽略了新闻内容与用户先验知识之间的关联，导致推荐结果缺乏多样性。本文提出了一种新颖的方法来解决新闻推荐这一复杂问题。我们的方法基于双重观察理念，即通过带有观察机制的深度神经网络识别新闻文章的核心焦点以及用户对文章的关注点。该机制通过考量反映用户个人兴趣与偏好的信念网络来实现。通过同时考虑新闻内容与用户视角，我们的方法能够提供更个性化和精准的推荐。我们在真实数据集上评估了模型性能，结果表明所提方法优于多种主流基线模型。