Fix Label column meaning in Recommendation sample

[sergey-tihon]

I think that the meaning of Label column is incorrect in your post

The Label column is our goal, it is what we want to predict, which in this case is mapped to the “CombinedProductID” field annotated with the Label ColumnName Attribute of the Product Record-type.

According to documentation for MatrixFactorizationTrainer Class

The coordinate descent method included is specifically for one-class matrix factorization where all observed ratings are positive signals (that is, all rating values are 1). Notice that the only way to invoke one-class matrix factorization is to assign one-class squared loss to loss function when calling MatrixFactorization(Options). See Page 6 and Page 28 here for a brief introduction to standard matrix factorization and one-class matrix factorization.

so the trainer expect input (sparse matrix) in the format

A.[ProductID, CombinedProductID] <- 1.0f

instead of this you provide matrix

A.[ProductID, CombinedProductID] <- CombinedProductID

A.[i,j] should represent likelihood of co-purchase, 1 means co-purchase and 0 is most likely no.

In this case trainer with factorize matrix and fill missing probabilities/likelyhood.

Page 28 of referenced paper also explain that https://www.csie.ntu.edu.tw/%7Ecjlin/talks/facebook.pdf

Output

With this fix, input matrix contain 0s and 1s and it is way more easier to factorize.

This is the result from your post

This is the result from this PR

RootMeanSquaredError and MeanSquaredError several order of magnitude smaller, but prediction for product 61 is similar =)

[rikace]

Well done!!