Various challenges, such as recommendation systems, have been tackled using deep learning, a subset of machine learning. In a sequential recommendation system, neural networks are employed to model the temporal dynamics of user activity. These systems leverage deep learning techniques to consider the context of past interactions and the time intervals between events, aiming to provide more accurate and personalized recommendations. Recurrent Neural Networks (RNNs) are frequently utilized in sequential recommendation due to their capability to capture sequential patterns and depict dependencies between items, enabling the prediction of future behavior. Another prevalent architecture for managing sequential data is Long Short-Term Memory (LSTM) Networks. Deep learning offers the advantage of effectively handling large-scale, high-dimensional data, enabling the learning of intricate, nonlinear representations. Deep neural networks also facilitate the representation of dynamic behaviors and allow for real-time adjustments in systems. Nonetheless, despite these advantages, deep learning-based sequential recommendation systems encounter significant challenges. One such challenge is the substantial data and processing resources often required, making them less suitable for small datasets. Additionally, the interpretability of these models poses another hurdle, as the complexity of deep learning models can hinder their interpretation and understanding, potentially impacting certain applications.