This paper presents a session-based recommendation system using recurrent neural networks (RNNs). Session-based recommendations are particularly challenging because they rely on short session data rather than long user histories. Traditional matrix factorization methods are not accurate in this context, and item-to-item recommendations are often used as a practical alternative. However, the authors argue that modeling the entire session can lead to more accurate recommendations. They propose an RNN-based approach that considers practical aspects of the task and introduces a ranking loss function tailored for this problem. The paper discusses the challenges of session-based recommendations, including the lack of user profiles and the need to handle sparse sequential data. It also compares RNNs with other methods such as factor models and neighborhood methods. The authors introduce a GRU-based RNN model, which is customized for session-based recommendations. They use session-parallel mini-batches and output sampling to improve performance. A ranking loss function is introduced to train the model effectively. The model is evaluated on two datasets: one from the RecSys Challenge 2015 and another from a YouTube-like video service. The results show that the RNN-based approach outperforms traditional baselines such as item-KNN and BPR-MF. The model achieves significant improvements in recall and MRR metrics, especially when using pairwise ranking losses. The authors conclude that RNNs can be a powerful tool for session-based recommendations and suggest further research into deep learning applications in this area.This paper presents a session-based recommendation system using recurrent neural networks (RNNs). Session-based recommendations are particularly challenging because they rely on short session data rather than long user histories. Traditional matrix factorization methods are not accurate in this context, and item-to-item recommendations are often used as a practical alternative. However, the authors argue that modeling the entire session can lead to more accurate recommendations. They propose an RNN-based approach that considers practical aspects of the task and introduces a ranking loss function tailored for this problem. The paper discusses the challenges of session-based recommendations, including the lack of user profiles and the need to handle sparse sequential data. It also compares RNNs with other methods such as factor models and neighborhood methods. The authors introduce a GRU-based RNN model, which is customized for session-based recommendations. They use session-parallel mini-batches and output sampling to improve performance. A ranking loss function is introduced to train the model effectively. The model is evaluated on two datasets: one from the RecSys Challenge 2015 and another from a YouTube-like video service. The results show that the RNN-based approach outperforms traditional baselines such as item-KNN and BPR-MF. The model achieves significant improvements in recall and MRR metrics, especially when using pairwise ranking losses. The authors conclude that RNNs can be a powerful tool for session-based recommendations and suggest further research into deep learning applications in this area.