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Re-bin avatar Re-bin commented on July 20, 2024 4

嗨,你好👋

感谢关注!这个是因为在SSLRec里面我们统一使用了all-rank(全排序)的测试方式,即对所有items进行排序,并且对top-k进行计算NDCG@K。在multi-behavior recommendation研究的原论文中(MATN, CML), 一般不是all-rank的测试策略,测试样本是由1个positive item+99个negative item构成(参考代码209-215行),也就是100个items进行排序,然后对其中top-k进行计算指标。这个是产生差异的主要原因。

其次,可能模型超参数也会有所影响,目前我们是通过Retail Rocket数据集对多行为算法的超参数调到最优。不同的数据集对应的最优超参数可能也会有所不同。

然后回到您提出这个例子上面,全排序和100个items排序,直觉上来说后者这个测试策略下的指标会比前者更高一些。但只要所有的算法都在同一个策略下进行公平的性能测试,就可以保证公平性。

希望可以帮到你,如果有好的建议也欢迎随时反馈 :)

Best regards,
Xubin

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GKONGJ avatar GKONGJ commented on July 20, 2024 1

非常非常感谢您的快速回复!!很详细很清晰 我已经明白啦 也再次感谢您团队提供的代码对我的科研提供了极大的帮助 祝周末愉快!祝好~

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