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大量信息,但通过大量新闻的筛选,对读者来说是压倒性的和累赘的。 个性化的建议算法可以帮助用户找到他们感兴趣的信息。 然而,大多数现有模式完全依赖于对用户行为的观察,例如查看历史,忽视新闻与用户先前的知识之间的联系。这可能导致缺乏针对个人的不同建议。在本文中,我们提出了一个解决复杂的新闻建议问题的新方法。我们的方法是基于双重观察的理念,即利用带有观察机制的深层神经网络来确定新闻文章的主要重点以及用户对文章的关注。这要通过考虑到用户的信仰网络来实现,这反映了他们的个人兴趣和偏见。通过考虑新闻的内容和用户的观点,我们的方法能够提供更个性化和准确的建议。我们评估了我们关于真实世界数据集的模型的绩效,并表明我们提出的方法超越了几个受欢迎的基线。