This paper explores the application of Recurrent Neural Networks (RNNs) in session-based recommendation systems, a domain where traditional matrix factorization methods often fall short due to the limited user histories available. The authors propose an RNN-based approach that models the entire session to provide more accurate recommendations. They introduce several modifications to classic RNNs, including a ranking loss function, to make the model more suitable for this specific task. Experimental results on two datasets—RecSys Challenge 2015 and a YouTube-like OTT video service—show significant improvements over widely used approaches, demonstrating the effectiveness of their proposed method. The paper also discusses the practical aspects of the task, such as session-parallel mini-batches and output sampling, and provides insights into the best hyperparameters and network architectures for this application.This paper explores the application of Recurrent Neural Networks (RNNs) in session-based recommendation systems, a domain where traditional matrix factorization methods often fall short due to the limited user histories available. The authors propose an RNN-based approach that models the entire session to provide more accurate recommendations. They introduce several modifications to classic RNNs, including a ranking loss function, to make the model more suitable for this specific task. Experimental results on two datasets—RecSys Challenge 2015 and a YouTube-like OTT video service—show significant improvements over widely used approaches, demonstrating the effectiveness of their proposed method. The paper also discusses the practical aspects of the task, such as session-parallel mini-batches and output sampling, and provides insights into the best hyperparameters and network architectures for this application.