predict_rank method and test set role

[jwheatp]

Hi,

I'm trying to understand deeply how LightFM works. However, a part is still a bit confused for me : it is the predict_rank function.

Here is my question :
Could you explain again, in the predict_rank input, how is structured the test_interactions (compared to train_interactions) frame and what is his role in the function ?
This is what I understood : in the test_set, if we set an interaction to non-zero (for ex. 1) for a user u and an item i, the model will make a recommendation for user u, for all items, and see in which position the item i is ranked.
Am I right ? Maybe I misunderstood the 'rank' definition.

I will start with this question, and I may have other more later :)

Thanks in advance,

Julien

[maciejkula]

Yes, this is correct. This is a helper function for computing various ranking-related evaluation scores where you need to know the position (in the full ranked list for any given user) of the test interactions.

[jwheatp]

Thanks for your answer.

[dwy904]

How can I calculate the original interaction back and make recommendation? do I just simply do
np.matmul(model_bpr.user_embeddings, model_bpr.item_embeddings.transpose()) ?