This paper explores the area of news recommendation, a critical component of online information sharing. It introduces the core problem and reviews current methods and recent algorithms. The authors present their implementation of NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and evaluate its effectiveness. The evaluation shows that NRAM has the potential to significantly improve personalized news content for users on digital platforms. The paper is structured into several sections: an introduction to news recommendation, problem formulation, news recommendation algorithms, datasets, and the implementation of NRAM. The attention mechanism, including self-attention and additive attention, is detailed, and the NRAM model is described, which uses multi-head self-attention and an additive attention module to aggregate user and news representations. The click prediction module and training process are also explained. The authors use the MIND dataset, a benchmark developed by Microsoft, and explore its behavior and news data. They conduct experiments and compare their model (NRAM) with a baseline (DKN), demonstrating that NRAM outperforms the baseline, highlighting the effectiveness of the attention mechanism. Future work includes using more powerful language models and incorporating additional features to further improve performance. The paper concludes by summarizing the key contributions and future directions, emphasizing the power of attention mechanisms in handling natural language information.This paper explores the area of news recommendation, a critical component of online information sharing. It introduces the core problem and reviews current methods and recent algorithms. The authors present their implementation of NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and evaluate its effectiveness. The evaluation shows that NRAM has the potential to significantly improve personalized news content for users on digital platforms. The paper is structured into several sections: an introduction to news recommendation, problem formulation, news recommendation algorithms, datasets, and the implementation of NRAM. The attention mechanism, including self-attention and additive attention, is detailed, and the NRAM model is described, which uses multi-head self-attention and an additive attention module to aggregate user and news representations. The click prediction module and training process are also explained. The authors use the MIND dataset, a benchmark developed by Microsoft, and explore its behavior and news data. They conduct experiments and compare their model (NRAM) with a baseline (DKN), demonstrating that NRAM outperforms the baseline, highlighting the effectiveness of the attention mechanism. Future work includes using more powerful language models and incorporating additional features to further improve performance. The paper concludes by summarizing the key contributions and future directions, emphasizing the power of attention mechanisms in handling natural language information.