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*
与基线比较的数值评估在判断推荐系统中的研究时起着核心作用。在本文中,我们证明了正确运行基线是困难的。我们在两个广泛研究的数据集上证明了这个问题。首先,我们表明,在过去五年中,在许多出版物中使用的基线对Movielens
10M基准的结果是次优的。通过仔细设置一个普通矩阵分解基线,我们不仅能够改进该基线的报告结果,而且甚至优于任何新提出的方法的报告结果。其次,我们回顾了社区为在Netflix
Prize上以简单的方法获得高质量结果所付出的巨大努力。我们的结果表明,研究论文中的实证发现是有问题的,除非它们是在标准化基准上获得的,其中基线已被研究界广泛调整。
*
在推荐系统领域,数值评价在评判研究中起着核心作用。期望将新发表的方法与基线(即众所周知的方法)进行比较,以便衡量对先前工作的改进。最佳实践需要在多个数据集上进行可重复的实验,具有明确描述的评估方案,通过超参数搜索调整基线,并测试结果的统计显著性。这些实验的结果被认为是可靠的。在这项工作中,我们质疑这种做法,并表明正确运行基线是困难的。
* 我们在广泛研究的Movielens 10M
(ML10M)基准上强调了这个问题[11]。在过去的五年中,许多新的推荐算法已经在诸如ICML等著名会议上发表[17,21