Intelligent personalized content recommendations based on neural networks

To effectively assist users in discovering content tailored to their specific interests, this research aims to create an intelligent content recommendation system. The inadequacy of conventional recommendation models, which depend uniquely on historical reading data, becomes evident in their limited capacity to meet contemporary users' diverse and ever-changing preferences within the information. The proposed architecture makes the most of the advancements in deep learning technology. It integrates the self-attention mechanism, allowing for precise calibration of the significance attributed to each feature within the news data. The proposed multilevel data classification network enables a more refined and personalized knowledge of users' preferences and the array of content information attributes while incorporating the users' unique characteristics. The proposed model achieved an accuracy rate of 85.2%, a recall rate of 83.7%, an F1 score of 84.3%, and an Area Under the Curve (AUC) of 84.5%. By developing a multilevel, intelligent, personalized content recommendation network, the research attempts to introduce a solution that effectively provides users' preferences, thereby enriching their experience in discovering relevant information within the modern digital system.