This paper explores the area of news recommendation, a key component of online information sharing. The authors introduce NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Their evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms. News recommendation systems face several challenges, including the transient nature of news content, the need for sophisticated natural language processing techniques to understand news content, and the challenge of deducing user preferences from implicit signals. The authors propose a model that uses multi-head self-attention to model both user and news representations, followed by an additive attention module as an aggregator. The model calculates the dot product of user and news representations to obtain click probability, and the training process is end-to-end. The authors use the MIND dataset, a benchmark developed by Microsoft, which contains user click logs and news articles. They evaluate their model against a baseline, DKN, and find that their model with attention outperforms previous baselines. The model uses self-attention and additive attention mechanisms to learn representations of both user and news content. They also use negative sampling to balance the training process and transform the problem into a triplet classification task. The authors conclude that the attention mechanism is effective in handling natural language information and that NRAM can achieve good results on different metrics. They suggest future work, including the use of more powerful language models and more features to improve performance on the MIND dataset. The authors also mention that their model is implemented in PyTorch and that they used an early stopping strategy to alleviate the workload.This paper explores the area of news recommendation, a key component of online information sharing. The authors introduce NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Their evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms. News recommendation systems face several challenges, including the transient nature of news content, the need for sophisticated natural language processing techniques to understand news content, and the challenge of deducing user preferences from implicit signals. The authors propose a model that uses multi-head self-attention to model both user and news representations, followed by an additive attention module as an aggregator. The model calculates the dot product of user and news representations to obtain click probability, and the training process is end-to-end. The authors use the MIND dataset, a benchmark developed by Microsoft, which contains user click logs and news articles. They evaluate their model against a baseline, DKN, and find that their model with attention outperforms previous baselines. The model uses self-attention and additive attention mechanisms to learn representations of both user and news content. They also use negative sampling to balance the training process and transform the problem into a triplet classification task. The authors conclude that the attention mechanism is effective in handling natural language information and that NRAM can achieve good results on different metrics. They suggest future work, including the use of more powerful language models and more features to improve performance on the MIND dataset. The authors also mention that their model is implemented in PyTorch and that they used an early stopping strategy to alleviate the workload.