The evaluation of recommender systems from a practical perspective is a topic of ongoing discourse within the research community. While many current evaluation methods reduce performance to a single value metric as an easy way to compare models, it relies on the assumption that the methods' performance remains constant over time. In this study, we examine this assumption and propose the Cross-Validation Thought Time (CVTT) technique as a more comprehensive evaluation method, focusing on model performance over time. By utilizing the proposed technique, we conduct an in-depth analysis of the performance of popular RecSys algorithms. Our findings indicate that (1) the performance of the recommenders varies over time for all reviewed datasets, (2) using simple evaluation approaches can lead to a substantial decrease in performance in real-world evaluation scenarios, and (3) excessive data usage can lead to suboptimal results.