Exploration on News Recommendation Model under Machine Learning and Knowledge Graph Technology

Conventional news recommendation systems rely on generic factors like popularity, similarity, or trending topics to suggest content, overlooking the personalized requirements and preferences of individual users. As a result, there is a lack of wide-ranging and diverse news suggestions, which means users' individualized reading preferences are not being adequately catered to. To tackle these challenges, the article employs cutting-edge techniques like machine learning and knowledge graph technology to construct a model that accurately depicts the new problem. Furthermore, it extensively collects and preprocesses a significant volume of news data, meticulously cleansing and transforming it. In parallel, the article harnesses feature extraction methods like the bag-of-words model, Term Frequency Inverse Document Frequency (TF-IDF), and word embedding to convert the news text into numerical feature vectors. This allows for the comprehensive representation of the news, capturing its underlying semantics and crucial information. Through rigorous experimentation, the integration of machine learning and knowledge graph technology within the news recommendation model has proven to be exceptionally effective, delivering exceptional results in terms of accuracy and user satisfaction. Achieving an impressive accuracy rate of up to 95%, this approach has surpassed expectations. Through the integration of machine learning algorithms and knowledge graphs, this article empowers itself to offer more precise and personalized news recommendations tailored to users' interests and preferences. It doesn't stop there, though. By consistently training and optimizing the underlying models with valuable user feedback, the article continues to enhance its recommendation algorithms, ensuring an increasingly accurate and satisfactory user experience.