Sequential recommendation is a research area within the field of recommender systems that focuses on predicting the next item or action that a user will interact with or engage in based on their past behaviors, preferences, and interactions. This type of recommendation is particularly useful in scenarios where users' preferences evolve over time or where the order in which items are presented or recommended is important. Sequential recommendation algorithms often use techniques such as sequential pattern mining, Markov models, and recurrent neural networks to model the temporal dependencies in users' behavior and make personalized recommendations. By taking into account the context and sequence of users' past interactions, sequential recommendation systems can provide more accurate and relevant recommendations compared to traditional recommender systems.