论文标题
探索数据拆分策略以评估推荐模型
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
论文作者
论文摘要
评估推荐系统的有效方法至关重要，因此可以以合理的方式比较此类系统。推荐系统评估的一个普遍忽略的方面是选择数据拆分策略。在本文中，我们俩都表明没有标准的拆分策略，并且选择分裂策略可以对推荐系统的排名产生强大的影响。特别是，我们进行了比较三种常见分裂策略的实验，研究了它们对两个数据集的七个最先进的建议模型的影响。我们的结果表明，采用的分裂策略是一个重要的混杂变量，可以显着改变最先进的系统的排名，即使使用相同的数据集和指标，也使当前发表的文献中的许多不可避免。
Effective methodologies for evaluating recommender systems are critical, so that such systems can be compared in a sound manner. A commonly overlooked aspect of recommender system evaluation is the selection of the data splitting strategy. In this paper, we both show that there is no standard splitting strategy and that the selection of splitting strategy can have a strong impact on the ranking of recommender systems. In particular, we perform experiments comparing three common splitting strategies, examining their impact over seven state-of-the-art recommendation models for two datasets. Our results demonstrate that the splitting strategy employed is an important confounding variable that can markedly alter the ranking of state-of-the-art systems, making much of the currently published literature non-comparable, even when the same dataset and metrics are used.
