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.
Graph neural networks have been widely used in many fields. Most studies are used to solve the node classification task, but most of the time the distribution of the data is unbalanced, which affects the classification accuracy of the model. In this paper, we balance the data distribution in the graph by generating new samples in the embedded space and introducing random variables to control the spatial distance between the new samples and the target samples. We also propose a framework to solve the unbalanced node classification task in the graph, and the experimental results show that the method is effective.
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