PERSONALIZATION OF NEWS CONTENT USING AI: ARCHITECTURE AND EFFICIENCY OF NEURAL NETWORK SYSTEMS
Authors
Shalamov Igor Stanislavovich
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The rapid growth of digital information has made news content personalization a critical tool for addressing information overload and enhancing user engagement. This study investigates the architecture and efficiency of neural network systems for personalizing news content, focusing on a proposed hybrid model combining BERT for semantic text analysis and LSTM for modeling temporal user interactions. Using the MIND dataset, we compared the hybrid model against baseline architectures (TextCNN, LSTM, BERT-base) and traditional matrix factorization. The hybrid model achieved superior performance, with Precision@5 of 0.78, Recall@5 of 0.75, F1-score of 0.76, and a click-through rate (CTR) of 6.3%, outperforming baselines by up to 43% in F1-score and 66% in CTR. The model demonstrated robustness to noisy data and improved recommendations for cold-start scenarios. However, challenges such as computational complexity and applicability to multilingual datasets remain. The findings highlight the potential of hybrid neural architectures for real-world news personalization while underscoring the need to address ethical concerns, such as filter bubbles and privacy. Future research should explore multimodal data integration and lightweight models to enhance scalability and diversity in recommendations.
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Authors
Shalamov Igor Stanislavovich
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