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cuvk是权重,k+表示正样例的权重,k-表示负样例的权重。论文里面是以一个比较general的方式来写的,实际在现有工作中为了减少调参的难度,一般会将正样例cuv+的值设为‘1’,而只调整cuv-的值。对于cuv-的值的设置又存在多种方法,比如最简单的将cuv-设为一个固定的在0-1之间的值(如之前的WMF的工作),或者是将cuv-计算为一个跟商品popularity相关的值(cv-,如之前的eals)。
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如果是对传统单行为推荐场景感兴趣的话,可以参考我们写的更详细的期刊论文Efficient Neural Matrix Factorization without Sampling for Recommendation
感恩作者大人
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