How does surprise specifically deals with the cold-start problem?

[vitor-rolla]

Hi Nicolas,

Surprise is great!

I have a dataset with a long-long tail, which means that I should get many cold-start problems after splitting the dataset.

I expected to have a low AUC, but I am getting a relatively high AUC (83%) on the test set.

So, how does surprise specifically deals with the cold-start problem? Does it recommend any (or random) item to a new user? Or, the new user is kept out of the metric calculation?

I couldn't find such info in the documents.

Thank you very much for your attention.

Regards,

Vitor

[NicolasHug]

The algorithms in surprise don't have a fallback to specifically handle cold start but some (e.g. SVD) will naturally fallback to the mean of the ratings if there are no user-wise or item-wise info available