I have a question with regard to the predict function of the FactorizationRecommender.
At my disposal, I have a large dataset with user item pairs (and a binary rating for each pair). Important to note is that users have not interacted with all items (the rating matrix is very sparse).
Subsequently, I remove all ratings of one user (I choose him/her to be the cold user) from the dataset. On all remaining user item pairs I train a matrix factorization model (factorization_recommender.create(...,binary_target=True)).
Now, I would like to make predictions for the remaining ratings of the cold user when I show the model a fraction of the cold user's ratings (e.g., I show the model 10 of the cold user's ratings and want to compute predicted ratings for all other items). Next I want to compute the RMSE of the predictions ONLY for the cold user.
My question is two-fold. First of all, it is not entirely clear to me which arguments to pass to the FactorizationRecommender.predict function.
The fraction of the user item pairs (and binary ratings) that I want to show to the model (e.g., the 10 ratings), should these be the new_observation_data? And what should my input be for the dataset? The initial training dataset?
Secondly, my question is how the FactorizationRecommender.predict function precisely works (what's happening in the background)? How can you make predictions on a user that is not included in the initial training dataset? As the latent factors of the factorization are not built for this user, how are his/her predictions made?
My current version of GraphLab Create is v1.10.1.
Thanks for your help!
