Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations
Anton Klenitskiy, Anna Volodkevich, Anton Pembek, Alexey Vasilev · 2024
Sequential recommender systems are an important and demanded area of research. Such systems aim to use the order of interactions in a user’s history to predict future interactions. The premise is that the order of interactions and sequential patterns play an essential role. Therefore, it is crucial to use datasets that exhibit a sequential structure to evaluate sequential recommenders properly.