MLLib spark -ALStrainImplicit 值大于 1 [英] MLLib spark -ALStrainImplicit value more than 1

问题描述

使用 Spark mllib ALS("trainImplicit") 进行了一段时间的试验.想了解一下

Experimenting with Spark mllib ALS("trainImplicit") for a while now. Would like to understand

1.为什么我在预测中得到的评分值大于 1?

1.why Im getting ratings value more than 1 in the predictions?

2.是否需要规范化用户产品输入?

2.Is there any need for normalizing the user-product input?

示例结果:

[评分(用户=316017，产品=114019，评分=3.1923)，

[Rating(user=316017, product=114019, rating=3.1923),

评分(用户=316017，产品=41930，评分=2.0146997092620897)]

Rating(user=316017, product=41930, rating=2.0146997092620897) ]

在文档中，提到预测的评级值将在 0-1 左右.我知道评分值仍可用于推荐，但如果我知道原因就太好了.

In the documentation, it is mentioned that the predicted rating values will be somewhere around 0-1. I know that the ratings values can still be used in recommendations but it would be great if I know the reason.

推荐答案

ALS trainImplicit() 中的成本函数不对预测评分值强加任何条件，因为它采用了与 0/1 之间的差异幅度.因此，您可能还会在那里找到一些负值.这就是为什么它说预测值在 [0,1] 左右，但不一定在其中.

The cost function in ALS trainImplicit() doesn't impose any condition on predicted rating values as it takes the magnitude of difference from 0/1. So, you may also find some negative values there. That is why it says the predicted values are around [0,1] not necessarily in it.

有一个选项可以仅设置非负因式分解，因此您永远不会在预测评分或特征矩阵中得到负值，但这似乎会降低我们案例的性能.

There is one option to set non-negative factorization only, so that you never get a negative value in predicted rating or feature matrices, but that seemed to drop the performance for our case.

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