问题
Experimenting with Spark mllib ALS("trainImplicit") for a while now. Would like to understand
1.why Im getting ratings value more than 1 in the predictions?
2.Is there any need for normalizing the user-product input?
sample result:
[Rating(user=316017, product=114019, rating=3.1923),
Rating(user=316017, product=41930, rating=2.0146997092620897) ]
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.
回答1:
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.
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.
来源：https://stackoverflow.com/questions/36275600/mllib-spark-alstrainimplicit-value-more-than-1