Personalized news suggestions are an important technology to enhance people’s online news reading experiences. How to better understand users and news representation is a major issue in news recommendation. The majority of cutting-edge news recommendation techniques mostly neglect the link between title and content, explicitly and implicitly. They neglect to take into account the effects of many prospective news preferences on people’s behavior when they click on various news items. We first build a user-news interaction graph and then present the weight learning and preference decomposition (WLPD) news recommendation model for graph neural networks, which is based on WLPD. This model not only takes into account the impact of the relationship between news titles and content, explicit and implicit, on the likelihood that users will click on the news, but also takes into account the various potential preferences between users and news interaction. Finally, using actual news databases, we run a number of experiments. We discover that our model significantly improved in terms of accuracy and performance compared with other cutting-edge news recommendation techniques.

中文翻译：

---

基于权重学习和偏好分解的图神经网络新闻推荐

个性化新闻推荐是增强人们在线新闻阅读体验的重要技术。如何更好地理解用户和新闻表征是新闻推荐的一个主要问题。大多数前沿新闻推荐技术大多忽视了标题和内容之间显式或隐式的联系。他们忽略了许多预期新闻偏好对人们点击各种新闻项目时的行为的影响。我们首先构建用户新闻交互图，然后提出基于 WLPD 的图神经网络权重学习和偏好分解（WLPD）新闻推荐模型。该模型不仅考虑了新闻标题与内容之间的显式和隐式关系对用户点击新闻可能性的影响，还考虑了用户与新闻交互之间的各种潜在偏好。最后，使用实际的新闻数据库，我们进行了一些实验。我们发现，与其他尖端新闻推荐技术相比，我们的模型在准确性和性能方面都有显着提高。