Research And Implementation Of News Recommendation System Based On Feature Learining And Preference Learning

With the continuous development of the Internet,people are faced with various options for accessing news information.However,due to the large number of news articles and high repetition rate of information,individuals often struggle to quickly find news that aligns with their interests.In order to address this issue,news recommendation systems have emerged,which analyze users’ reading history and interests to provide personalized news recommendations.When it comes to personalized news recommendation,there are two effective approaches that can be adopted: modeling feature interactions and mining user historical preferences.However,traditional methods for modeling feature interactions are complex,making it difficult to accurately capture user interests and failing to fully utilize collaborative information from user behavior history.Therefore,this paper proposes a model based on graph neural networks that combines feature learning and preference learning,and designs and implements a personalized news recommendation system based on this model.In terms of news recommendation algorithms,this paper presents a feature learning and preference learning recommendation model based on graph neural networks.The model employs two key methods to improve recommendation performance.Firstly,the model constructs a complete graph of user and news features and learns the embedding representations of users and news through feature interactions.Secondly,to better understand user preferences,the model constructs a user-news high-order connectivity graph based on the interaction history between users and news.By propagating multi-hop information in the graph,the model captures user preference information.This preference learning method comprehensively understands user preferences and behavior patterns.Finally,the model matches the learned user and news embedding representations to make recommendation predictions.To validate the effectiveness and rationality of the model,experiments were conducted on four public datasets,including news datasets.The results demonstrate the significant performance improvement of the proposed model in the recommendation task.In terms of system design,this paper designs and implements a personalized news recommendation system based on feature learning and preference learning.The system architecture consists of data layer,feature layer,strategy layer,and application layer.In the data layer,the system obtains the latest news through news crawling to ensure the timeliness of the news material database.In the strategy layer,a news sorting strategy based on feature learning and preference learning is adopted.For cold start scenarios,the system fills the recommendation list for new users by ranking the popularity of the material database.Furthermore,to prevent excessive exposure of similar news,the system applies reordering based on news types.The personalized recommendation system integrates multiple aspects to ensure the accuracy,timeliness,and diversity of news recommendations.