论文标题
CVTT：随时间的交叉验证
CVTT: Cross-Validation Through Time
论文作者
论文摘要
从实际角度来看，对推荐系统的评估是研究界持续讨论的话题。尽管许多当前的评估方法将性能降低为单个值度量，以作为比较模型的简便方法，但它依赖于该方法的性能随着时间的推移保持恒定的假设。在这项研究中，我们研究了这一假设，并提出了交叉验证思维时间（CVTT）技术作为一种更全面的评估方法，重点是随时间推移模型性能。通过利用所提出的技术，我们对流行Recsys算法的性能进行了深入的分析。我们的发现表明，（1）推荐人的性能随时间而变化，（2）使用简单的评估方法可以导致实际评估方案的性能大幅下降，并且（3）过多的数据使用可能会导致次优结果。
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.
