Traditionally, deep learning models’ training involves choosing complex network architectures and large data sets to build models with high accuracy. This kind of training demands a large high-performance computing infrastructure to complete the training process in a reasonable time. We explore a data-centric approach to choose the “right” data samples in the “right” amount during each epoch of a model’s training to build the model efficiently in a shorter time and at least with the same (sometimes even better) accuracy compared to the traditional approach. This paper presents our experience using domain knowledge (temporal nature of data) to build a recommendation model by reducing data samples used in successive training epochs. We show that using a data-centric approach on state-of-the-art session-based recommendation models can reduce the model training time by at least 2.1x and achieve slightly better accuracy and “average recommendation "popularity" on a publicly available data set containing 6 million sessions.