Collaborative filtering test predictions

Part 1 (2018)
2

Hi,

that’s a good question. I would be interested also in it.
I’ve come up with following:

  1. Create a model which is ready to make predictions
  2. Create the user-item combinations for which you need predictions. For the ratings add some value. Stackoverflow how to do this.
  3. Get the correct ids for the newly created combinations for the val_idxs parameter. Make sure the new combinations are in the validation set.
  4. Create a new data loader object ( e.g. CollabFilterDataset.from_csv). Let’s call it new_dl
  5. Set the new DL for the model just like in lesson2 .-> “learn.set_data(new_dl)”
  6. Get predictions by “predict(learn.model, learn.data.val_dl)”

I have not tried it in practice. But I think it should work for existing users and items that they have not rated.
For new users check out the other thread on this topic.
Let me know if it works.